| Type: | Package |
| Title: | Population (and Individual) Optimal Experimental Design |
| Version: | 0.7.0 |
| Depends: | R (≥ 2.14) |
| Imports: | ggplot2, MASS, mvtnorm, dplyr (≥ 0.7.0), codetools, stats, utils, magrittr, boot, purrr, stringr, tibble, gtools |
| Suggests: | testthat, Hmisc, nlme, GA, deSolve, Rcpp, shiny, rhandsontable, knitr, rmarkdown, gridExtra, covr, devtools, mrgsolve |
| Description: | Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>. |
| License: | LGPL (≥ 3) |
| ByteCompile: | true |
| URL: | https://andrewhooker.github.io/PopED/, https://github.com/andrewhooker/PopED |
| BugReports: | https://github.com/andrewhooker/PopED/issues |
| Copyright: | 2014-2021 Andrew C. Hooker |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| VignetteBuilder: | knitr |
| Config/Needs/website: | mrgsolve, kableExtra, PKPDsim, rxode2 |
| NeedsCompilation: | no |
| Packaged: | 2024-10-07 18:55:02 UTC; ancho179 |
| Author: | Andrew C. Hooker 
[aut, cre, trl, cph],
Marco Foracchia [aut] (O-Matrix version),
Eric Stroemberg [ctb] (MATLAB version),
Martin Fink [ctb] (Streamlining code, added functionality, vignettes),
Giulia Lestini [ctb] (Streamlining code, added functionality,
vignettes),
Sebastian Ueckert
[aut] (MATLAB version),
Joakim Nyberg [aut] (MATLAB version) |
| Maintainer: | Andrew C. Hooker <andrew.hooker@farmaci.uu.se> |
| Repository: | CRAN |
| Date/Publication: | 2024-10-07 19:30:02 UTC |
PopED - Population (and individual) optimal Experimental Design.
Description
PopED computes optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix (FIM).
Details
To get started you need to define
A model.
An initial design (and design space if you want to optimize).
The tasks to perform.
There are a number of functions to help you with these tasks. The user-level functions defined below are meant to be run with a minimum of arguments (for beginners to advanced users). Many of the other functions in the package (and not listed here) are called by these user-level functions and are often not as user friendly (developer level or advanced user functions).
Define a structural model:
ff.PK.1.comp.oral.md.CL,
ff.PK.1.comp.oral.md.KE,
ff.PK.1.comp.oral.sd.CL,
ff.PK.1.comp.oral.sd.KE,
ff.PKPD.1.comp.oral.md.CL.imax,
ff.PKPD.1.comp.sd.CL.emax.
Define a residual unexplained variability model (residual error model):
feps.add.prop,
feps.add,
feps.prop.
Create an initial study design (and design space):
create.poped.database.
Evaluate the model and/or design through simulation and graphics:
plot_model_prediction,
model_prediction,
plot_efficiency_of_windows.
Evaluate the design using the FIM:
evaluate_design,
evaluate.fim,
evaluate.e.ofv.fim,
ofv_fim,
get_rse.
Optimize the design (evaluate afterwards using the above functions):
poped_optim,
See the "Examples" section below for a short introduction to using the above functions. There are several other examples, as r-scripts, in the "examples" folder in the PopED installation directory located at (run at the R command line):
system.file("examples", package="PopED").
Author(s)
Maintainer: Andrew C. Hooker andrew.hooker@farmaci.uu.se (ORCID) [translator, copyright holder]
Authors:
Marco Foracchia (O-Matrix version)
Sebastian Ueckert (ORCID) (MATLAB version)
Joakim Nyberg (MATLAB version)
Other contributors:
Eric Stroemberg (MATLAB version) [contributor]
Martin Fink (Streamlining code, added functionality, vignettes) [contributor]
Giulia Lestini (Streamlining code, added functionality, vignettes) [contributor]
References
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software for optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
See Also
Useful links:
Report bugs at https://github.com/andrewhooker/PopED/issues
Examples
library(PopED)
##-- Model: One comp first order absorption
## -- Analytic solution for both mutiple and single dosing
ff <- function(model_switch,xt,parameters,poped.db){
with(as.list(parameters),{
y=xt
N = floor(xt/TAU)+1
y=(DOSE*Favail/V)*(KA/(KA - CL/V)) *
(exp(-CL/V * (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) -
exp(-KA * (xt - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
return(list( y=y,poped.db=poped.db))
})
}
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
return( parameters )
}
## -- Residual unexplained variablity (RUV) function
## -- Additive + Proportional
feps <- function(model_switch,xt,parameters,epsi,poped.db){
returnArgs <- do.call(poped.db$model$ff_pointer,list(model_switch,xt,parameters,poped.db))
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
y = y*(1+epsi[,1])+epsi[,2]
return(list( y= y,poped.db =poped.db ))
}
## -- Define design and design space
poped.db <- create.poped.database(ff_fun=ff,
fg_fun=sfg,
fError_fun=feps,
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9),
notfixed_bpop=c(1,1,1,0),
d=c(V=0.09,KA=0.09,CL=0.25^2),
sigma=c(0.04,5e-6),
notfixed_sigma=c(0,0),
m=2,
groupsize=20,
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
bUseGrouped_xt=1,
a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24)),
maxa=c(DOSE=200,TAU=24),
mina=c(DOSE=0,TAU=24))
## create plot of model without variability
plot_model_prediction(poped.db, model_num_points = 300)
## Not run:
## create plot of model with variability
plot_model_prediction(poped.db, IPRED=T, DV=T, separate.groups=T, model_num_points = 300)
## End(Not run)
## evaluate initial design
evaluate_design(poped.db)
## Not run:
# Optimization of sample times
output <- poped_optim(poped.db, opt_xt=TRUE, parallel = TRUE)
summary(output)
get_rse(output$FIM, output$poped.db)
plot_model_prediction(output$poped.db)
# Optimization of sample times and doses
output_2 <- poped_optim(poped.db, opt_xt=TRUE, opt_a=TRUE, parallel = TRUE)
summary(output_2)
get_rse(output_2$FIM,output_2$poped.db)
plot_model_prediction(output_2$poped.db)
# Optimization of sample times with only integer time points in design space
# faster than continuous optimization in this case
poped.db.discrete <- create.poped.database(ff_fun=ff,
fg_fun=sfg,
fError_fun=feps,
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9),
notfixed_bpop=c(1,1,1,0),
d=c(V=0.09,KA=0.09,CL=0.25^2),
sigma=c(0.04,5e-6),
notfixed_sigma=c(0,0),
m=2,
groupsize=20,
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
discrete_xt = list(0:248),
bUseGrouped_xt=1,
a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24)),
maxa=c(DOSE=200,TAU=24),
mina=c(DOSE=0,TAU=24),
ourzero = 0)
output_discrete <- poped_optim(poped.db.discrete, opt_xt=T, parallel = TRUE)
summary(output_discrete)
get_rse(output_discrete$FIM,output_discrete$poped.db)
plot_model_prediction(output_discrete$poped.db)
# Efficiency of sampling windows
plot_efficiency_of_windows(output_discrete$poped.db,xt_windows=0.5)
plot_efficiency_of_windows(output_discrete$poped.db,xt_windows=1)
## End(Not run)
D-family optimization function
Description
Optimize the objective function. There are 4 different optimization algorithms used in this function
Adaptive random search. See
RS_opt.Stochastic gradient.
A Broyden Fletcher Goldfarb Shanno (BFGS) method for nonlinear minimization with box constraints.
A line search. See
a_line_search.
The
optimization algorithms run in series, taking as input the output from the
previous method. The stopping rule used is to test if the line search
algorithm fids a better optimum then its initial value. If so, then the chain
of algorithms is run again. If line search is not used then the argument
iter_tot defines the number of times the chain of algorithms is run.
This function takes information from the PopED database supplied as an
argument. The PopED database supplies information about the the model,
parameters, design and methods to use. Some of the arguments coming from the
PopED database can be overwritten; if they are supplied then they are used
instead of the arguments from the PopED database.
Usage
Doptim(
poped.db,
ni,
xt,
model_switch,
x,
a,
bpopdescr,
ddescr,
maxxt,
minxt,
maxa,
mina,
fmf = 0,
dmf = 0,
trflag = TRUE,
bUseRandomSearch = poped.db$settings$bUseRandomSearch,
bUseStochasticGradient = poped.db$settings$bUseStochasticGradient,
bUseBFGSMinimizer = poped.db$settings$bUseBFGSMinimizer,
bUseLineSearch = poped.db$settings$bUseLineSearch,
sgit = poped.db$settings$sgit,
ls_step_size = poped.db$settings$ls_step_size,
BFGSConvergenceCriteriaMinStep = poped.db$settings$BFGSConvergenceCriteriaMinStep,
BFGSProjectedGradientTol = poped.db$settings$BFGSProjectedGradientTol,
BFGSTolerancef = poped.db$settings$BFGSTolerancef,
BFGSToleranceg = poped.db$settings$BFGSToleranceg,
BFGSTolerancex = poped.db$settings$BFGSTolerancex,
iter_tot = poped.db$settings$iNumSearchIterationsIfNotLineSearch,
iter_max = 10,
...
)
Arguments
poped.db |
A PopED database. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
maxa |
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value |
mina |
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
trflag |
Should the optimization be output to the screen and to a file? |
bUseRandomSearch |
Use random search (1=TRUE, 0=FALSE) |
bUseStochasticGradient |
Use Stochastic Gradient search (1=TRUE, 0=FALSE) |
bUseBFGSMinimizer |
Use BFGS Minimizer (1=TRUE, 0=FALSE) |
bUseLineSearch |
Use Line search (1=TRUE, 0=FALSE) |
sgit |
Number of stochastic gradient iterations |
ls_step_size |
Number of grid points in the line search. |
BFGSConvergenceCriteriaMinStep |
BFGS Minimizer Convergence Criteria Minimum Step |
BFGSProjectedGradientTol |
BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance |
BFGSTolerancef |
BFGS Minimizer Line Search Tolerance f |
BFGSToleranceg |
BFGS Minimizer Line Search Tolerance g |
BFGSTolerancex |
BFGS Minimizer Line Search Tolerance x |
iter_tot |
Number of iterations to use if line search is not used. Must
be less than |
iter_max |
If line search is used then the algorithm tests if line
search (always run at the end of the optimization iteration) changes the
design in any way. If not, the algorithm stops. If yes, then a new
iteration is run unless |
... |
arguments passed to |
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software for optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Not run:
##############
# typically one will use poped_optimize
# This then calls Doptim for continuous optimization problems
##############
# RS+SG+LS optimization of sample times
# optimization with just a few iterations
# only to check that things are working
output <- poped_optimize(poped.db,opt_xt=T,
rsit=5,sgit=5,ls_step_size=5)
# RS+SG+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optimize(poped.db,opt_xt=T)
get_rse(output$fmf,output$poped.db)
plot_model_prediction(output$poped.db)
# Random search (just a few samples here)
rs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,rsit=20,
bUseRandomSearch= 1,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0)
# line search, DOSE and sample time optimization
ls.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 1,
ls_step_size=10)
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 1,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0,
sgit=20)
# BFGS search, DOSE and sample time optimization
bfgs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 1,
bUseLineSearch = 0)
##############
# If you really want to you can use Doptim dirtectly
##############
dsl <- downsizing_general_design(poped.db)
poped.db$settings$optsw[2] <- 1 # sample time optimization
output <- Doptim(poped.db,dsl$ni, dsl$xt, dsl$model_switch, dsl$x, dsl$a,
dsl$bpop, dsl$d, dsl$maxxt, dsl$minxt,dsl$maxa,dsl$mina)
## End(Not run)
Trace optimization routines
Description
A helper function for writing output to the screen and files when optimizing.
Usage
Dtrace(
fn,
it,
ni,
xtopt,
xopt,
aopt,
gxt,
ga,
dmf,
diff,
ixt,
ia,
itvector,
dmfvector,
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
rsit = poped.db$settings$rsit,
convergence_eps = poped.db$settings$convergence_eps
)
Arguments
fn |
A file to output information to. Can also be the screen if |
it |
the iteration number. |
ni |
A vector of the number of samples in each group. |
xtopt |
The matrix defining current best sampling schedule. |
xopt |
The cell structure defining the current best discrete design variables. |
aopt |
The matrix defining the current best continuous design variables. |
gxt |
The matrix defining the current gradient of the xt vector. |
ga |
The matrix defining the current gradient for the continuous design variables. |
dmf |
The current OFV. |
diff |
The difference from the previous iteration. |
ixt |
If xt Gradient Inversion occurred or not. |
ia |
If a Gradient Inversion occurred or not. |
itvector |
The iteration vector. Not currently used. |
dmfvector |
The dmf vector. Not currently used. |
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
rsit |
Number of Random search iterations |
convergence_eps |
Stochastic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal) |
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
FIM <- evaluate.fim(poped.db)
dmf <- det(FIM)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,diff=3,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=200)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,diff=3,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=0)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,diff=3,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=1)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,
diff=0,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=1)
Dtrace(fn="",
it=5,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,
diff=0,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=1)
Optimization function for D-family, E-family and Laplace approximated ED designs
Description
Optimize the objective function for D-family, E-family and Laplace approximated ED designs. Right now there is only one optimization algorithm used in this function
Adaptive random search. See
RS_opt.
This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
Usage
LEDoptim(
poped.db,
model_switch = NULL,
ni = NULL,
xt = NULL,
x = NULL,
a = NULL,
bpopdescr = NULL,
ddescr = NULL,
maxxt = NULL,
minxt = NULL,
maxa = NULL,
mina = NULL,
ofv_init = 0,
fim_init = 0,
trflag = TRUE,
header_flag = TRUE,
footer_flag = TRUE,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
out_file = NULL,
d_switch = FALSE,
use_laplace = T,
laplace.fim = FALSE,
use_RS = poped.db$settings$bUseRandomSearch,
...
)
Arguments
poped.db |
A PopED database. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
maxa |
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value |
mina |
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value |
ofv_init |
The initial OFV. If set to zero then it is computed. |
fim_init |
The initial value of the FIM. If set to zero then it is computed. |
trflag |
Should the optimization be output to the screen and to a file? |
header_flag |
Should the header text be printed out? |
footer_flag |
Should the footer text be printed out? |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
out_file |
Which file should the output be directed to? A string, a file handle using
|
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
laplace.fim |
Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done using the standard MC integration technique, so can be slow. |
use_RS |
should the function use a random search algorithm? |
... |
arguments passed to |
See Also
Other Optimize:
Doptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samoples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
# warfarin ed model
## Not run:
LEDoptim(poped.db)
LEDoptim(poped.db,opt_xt=T,rsit=10)
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE)
LEDoptim(poped.db,opt_xt=T,rsit=10,laplace.fim=TRUE)
LEDoptim(poped.db,opt_xt=T,rsit=10,use_laplace=FALSE)
## testing header and footer
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
out_file="foobar.txt")
ff <- LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
trflag=FALSE)
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
header_flag=FALSE)
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
out_file="")
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
footer_flag=FALSE)
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
footer_flag=FALSE, header_flag=FALSE)
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
footer_flag=FALSE, header_flag=FALSE,out_file="foobar.txt")
LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
footer_flag=FALSE, header_flag=FALSE,out_file="")
## End(Not run)
Model linearization with respect to epsilon.
Description
The function performs a linearization of the model with respect to the residual variability. Derivative of model w.r.t. eps evaluated at eps=0
Usage
LinMatrixH(model_switch, xt_ind, x, a, bpop, b_ind, bocc_ind, poped.db)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
xt_ind |
A vector of the individual/group sample times |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
b_ind |
vector of individual realization of the BSV terms b |
bocc_ind |
Vector of individual realizations of the BOV terms bocc |
poped.db |
A PopED database. |
Value
A matrix of size (samples per individual x number of epsilons)
See Also
Other FIM:
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
The linearized matrix L
Description
Function computes the derivative of the model with respect to the between subject variability terms in the model (b's and bocc's) evaluated at a defined point (b_ind and bocc_ind).
Usage
LinMatrixL(model_switch, xt_ind, x, a, bpop, b_ind, bocc_ind, poped.db)
Arguments
model_switch |
A vector that is the same size as xt, specifying which model each sample belongs to. |
x |
A vector for the discrete design variables. |
a |
A vector of covariates. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
b_ind |
The point at which to evaluate the derivative |
bocc_ind |
The point at which to evaluate the derivative |
poped.db |
A PopED database. |
Value
As a list:
y |
A matrix of size (samples per individual x number of random effects) |
poped.db |
A PopED database |
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
#for the FO approximation
ind=1
LinMatrixL(model_switch=t(poped.db$design$model_switch[ind,,drop=FALSE]),
xt_ind=t(poped.db$design$xt[ind,,drop=FALSE]),
x=zeros(0,1),
a=t(poped.db$design$a[ind,,drop=FALSE]),
bpop=poped.db$parameters$bpop[,2,drop=FALSE],
b_ind=zeros(poped.db$parameters$NumRanEff,1),
bocc_ind=zeros(poped.db$parameters$NumDocc,1),
poped.db)["y"]
Model linearization with respect to epsilon and eta.
Description
The function performs a linearization of the model with respect to the residual variability and then the between subject variability. Derivative of model w.r.t. eps then eta, evaluated at eps=0 and b=b_ind.
Usage
LinMatrixLH(
model_switch,
xt_ind,
x,
a,
bpop,
b_ind,
bocc_ind,
NumEPS,
poped.db
)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
xt_ind |
A vector of the individual/group sample times |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
b_ind |
vector of individual realization of the BSV terms b |
bocc_ind |
Vector of individual realizations of the BOV terms bocc |
NumEPS |
The number of eps() terms in the model. |
poped.db |
A PopED database. |
Value
A matrix of size (samples per individual x (number of sigma x number of omega))
See Also
Other FIM:
LinMatrixH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
#for the FOI approximation
ind=1
poped.db$settings$iApproximationMethod=3 # FOI approximation method
LinMatrixLH(model_switch=t(poped.db$design$model_switch[ind,,drop=FALSE]),
xt_ind=t(poped.db$design$xt[ind,,drop=FALSE]),
x=zeros(0,1),
a=t(poped.db$design$a[ind,,drop=FALSE]),
bpop=poped.db$parameters$bpop[,2,drop=FALSE],
b_ind=zeros(poped.db$parameters$NumRanEff,1),
bocc_ind=zeros(poped.db$parameters$NumDocc,1),
NumEPS=size(poped.db$parameters$sigma,1),
poped.db)["y"]
Model linearization with respect to occasion variability parameters.
Description
The function performs a linearization of the model with respect to the occasion variability parameter.. Derivative of model w.r.t. eta_occ, evaluated bocc_ind.
Usage
LinMatrixL_occ(
model_switch,
xt_ind,
x,
a,
bpop,
b_ind,
bocc_ind,
iCurrentOcc,
poped.db
)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
xt_ind |
A vector of the individual/group sample times |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
b_ind |
vector of individual realization of the BSV terms b |
bocc_ind |
Vector of individual realizations of the BOV terms bocc |
iCurrentOcc |
The current occasion. |
poped.db |
A PopED database. |
Value
A matrix of size (samples per individual x number of iovs)
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
#for the FO approximation
ind=1
# no occasion defined in this example, so result is zero
LinMatrixL_occ(model_switch=t(poped.db$design$model_switch[ind,,drop=FALSE]),
xt_ind=t(poped.db$design$xt[ind,,drop=FALSE]),
x=zeros(0,1),
a=t(poped.db$design$a[ind,,drop=FALSE]),
bpop=poped.db$parameters$bpop[,2,drop=FALSE],
b_ind=zeros(poped.db$parameters$NumRanEff,1),
bocc_ind=zeros(poped.db$parameters$NumDocc,1),
iCurrentOcc=1,
poped.db)["y"]
Optimize the objective function using an adaptive random search algorithm for D-family and E-family designs.
Description
Optimize the objective function using an adaptive random search algorithm.
Optimization can be performed for both D-family and E-family designs.
The function works for both discrete and continuous optimization variables.
This function takes information from the PopED database supplied as an argument.
The PopED database supplies information about the the model, parameters, design and methods to use.
Some of the arguments coming from the PopED database can be overwritten;
by default these arguments are NULL in the
function, if they are supplied then they are used instead of the arguments from the PopED database.
Usage
RS_opt(
poped.db,
ni = NULL,
xt = NULL,
model_switch = NULL,
x = NULL,
a = NULL,
bpopdescr = NULL,
ddescr = NULL,
maxxt = NULL,
minxt = NULL,
maxa = NULL,
mina = NULL,
fmf = 0,
dmf = 0,
trflag = TRUE,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
cfaxt = poped.db$settings$cfaxt,
cfaa = poped.db$settings$cfaa,
rsit = poped.db$settings$rsit,
rsit_output = poped.db$settings$rsit_output,
fim.calc.type = poped.db$settings$iFIMCalculationType,
approx_type = poped.db$settings$iApproximationMethod,
iter = NULL,
d_switch = poped.db$settings$d_switch,
use_laplace = poped.db$settings$iEDCalculationType,
laplace.fim = FALSE,
header_flag = TRUE,
footer_flag = TRUE,
out_file = NULL,
compute_inv = TRUE,
...
)
Arguments
poped.db |
A PopED database. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
maxa |
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value |
mina |
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
trflag |
Should the optimization be output to the screen and to a file? |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
cfaxt |
First step factor for sample times |
cfaa |
Stochastic Gradient search first step factor for covariates |
rsit |
Number of Random search iterations |
rsit_output |
Number of iterations in random search between screen output |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
approx_type |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI. |
iter |
The number of iterations entered into the |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
laplace.fim |
Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done using the standard MC integration technique, so can be slow. |
header_flag |
Should the header text be printed out? |
footer_flag |
Should the footer text be printed out? |
out_file |
Which file should the output be directed to? A string, a file handle using
|
compute_inv |
should the inverse of the FIM be used to compute expected RSE values? Often not needed except for diagnostic purposes. |
... |
arguments passed to |
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samoples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
# Just a few iterations, optimize on DOSE and sample times using the full FIM
out_1 <- RS_opt(poped.db,opt_xt=1,opt_a=1,rsit=3,fim.calc.type=0, out_file = "")
## Not run:
RS_opt(poped.db)
RS_opt(poped.db,opt_xt=TRUE,rsit=100,compute_inv=F)
RS_opt(poped.db,opt_xt=TRUE,rsit=20,d_switch=0)
RS_opt(poped.db,opt_xt=TRUE,rsit=10,d_switch=0,use_laplace=T)
RS_opt(poped.db,opt_xt=TRUE,rsit=10,d_switch=0,use_laplace=T,laplace.fim=T)
## Different headers and footers of output
RS_opt(poped.db,opt_xt=TRUE,rsit=10,out_file="foo.txt")
output <- RS_opt(poped.db,opt_xt=TRUE,rsit=100,trflag=FALSE)
RS_opt(poped.db,opt_xt=TRUE,rsit=10,out_file="")
RS_opt(poped.db,opt_xt=TRUE,rsit=10,header_flag=FALSE)
RS_opt(poped.db,opt_xt=TRUE,rsit=10,footer_flag=FALSE)
RS_opt(poped.db,opt_xt=TRUE,rsit=10,header_flag=FALSE,footer_flag=FALSE)
RS_opt(poped.db,opt_xt=TRUE,rsit=10,header_flag=FALSE,footer_flag=FALSE,out_file="foo.txt")
RS_opt(poped.db,opt_xt=TRUE,rsit=10,header_flag=FALSE,footer_flag=FALSE,out_file="")
## End(Not run)
Optimize using line search
Description
The function performs a grid search sequentially along design variables. The grid is defined by ls_step_size.
Usage
a_line_search(
poped.db,
out_file = "",
bED = FALSE,
diff = 0,
fmf_initial = 0,
dmf_initial = 0,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
ls_step_size = poped.db$settings$ls_step_size
)
Arguments
poped.db |
A PopED database. |
out_file |
The output file to write to. |
bED |
If the algorithm should use E-family methods. Logical. |
diff |
The OFV difference that is deemed significant for changing a design. If,
by changing a design variable the difference between the new and old OFV is less than |
fmf_initial |
The initial value of the FIM. If |
dmf_initial |
The initial value of the objective function value (OFV).
If |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
ls_step_size |
Number of grid points in the line search. |
Value
A list containing:
fmf |
The FIM. |
dmf |
The final value of the objective function value. |
best_changed |
If the algorithm has found a better design than the starting design. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
poped.db |
A PopED database. |
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
# very sparse grid to evaluate (4 points for each design valiable)
output <- a_line_search(poped.db, opt_xt=TRUE, opt_a=TRUE, ls_step_size=4)
## Not run:
# longer run time
output <- a_line_search(poped.db,opt_xt=TRUE)
# output to a text file
output <- a_line_search(poped.db,opt_xt=TRUE,out_file="tmp.txt")
## End(Not run)
Nonlinear minimization using BFGS with box constraints
Description
This is the implementation of a Broyden Fletcher Goldfarb Shanno (BFGS) method for nonlinear minimization with box constraints.
Usage
bfgsb_min(f_name, f_options, x0, l, u, options = list())
Arguments
f_name |
A function name (as a text string) that returns an objective function and the gradient of that objective function, in that order.
See |
f_options |
Options for the f_name argument. |
x0 |
the initial values to optimize |
l |
the lower bounds |
u |
the upper bounds |
options |
a list of additional settings arguments |
Value
A list containing:
x_k |
The objective function. |
f_k |
The gradient. |
B_k |
The hessian. |
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Not run:
# BFGS search, DOSE and sample time optimization
bfgs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=0,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 1,
bUseLineSearch = 0)
f_name <- 'calc_ofv_and_grad'
gen_des <- downsizing_general_design(poped.db)
aa <- 0*poped.db$settings$cfaa*matrix(1,poped.db$design$m,size(poped.db$design$a,2))
axt=1*poped.db$settings$cfaxt*matrix(1,poped.db$design$m,max(poped.db$design_space$maxni))
f_options_1 <- list(gen_des$x,1, 0, gen_des$model_switch,
aa=aa,axt=axt,poped.db$design$groupsize,
gen_des$ni,
gen_des$xt,gen_des$x,gen_des$a,gen_des$bpop[,2,drop=F],
getfulld(gen_des$d[,2,drop=F],poped.db$parameters$covd),
poped.db$parameters$sigma,
getfulld(poped.db$parameters$docc[,2,drop=F],
poped.db$parameters$covdocc),poped.db)
options=list('factr'=poped.db$settings$BFGSConvergenceCriteriaMinStep,
#'factr'=0.01,
'pgtol'=poped.db$settings$BFGSProjectedGradientTol,
'ftol'=poped.db$settings$BFGSTolerancef,
'gtol'=poped.db$settings$BFGSToleranceg,
'xtol'=poped.db$settings$BFGSTolerancex)
x_k=t(gen_des$xt)
lb=t(gen_des$minxt)
ub=t(gen_des$maxxt)
output <- bfgsb_min(f_name,f_options, x_k,lb,ub,options)
## End(Not run)
Summarize your experiment for optimization routines
Description
Create some output to the screen and a text file that summarizes the initial design and the design space you will use to optimize.
Usage
blockexp(
fn,
poped.db,
e_flag = FALSE,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[4],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5]
)
Arguments
fn |
The file handle to write to. |
poped.db |
A PopED database. |
e_flag |
Should output be with uncertainty around parameters? |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
See Also
Other Helper:
blockfinal(),
blockheader(),
blockopt()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
blockexp("",poped.db, opt_xt=TRUE)
Result function for optimization routines
Description
Create some output to the screen and a text file that summarizes the problem you solved.
Usage
blockfinal(
fn,
fmf,
dmf,
groupsize,
ni,
xt,
x,
a,
model_switch,
bpop,
d,
docc,
sigma,
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_inds = poped.db$settings$optsw[5],
fmf_init = NULL,
dmf_init = NULL,
param_cvs_init = NULL,
compute_inv = TRUE,
out_file = NULL,
trflag = TRUE,
footer_flag = TRUE,
run_time = NULL,
...
)
Arguments
fn |
The file handle to write to. |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
groupsize |
A vector of the number of individuals in each group. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
fmf_init |
Initial FIM. |
dmf_init |
Initial OFV. |
param_cvs_init |
The initial design parameter RSE values in percent. |
compute_inv |
should the inverse of the FIM be used to compute expected RSE values? Often not needed except for diagnostic purposes. |
out_file |
Which file should the output be directed to? A string, a file handle using
|
trflag |
Should the optimization be output to the screen and to a file? |
footer_flag |
Should the footer text be printed out? |
... |
arguments passed to |
See Also
Other Helper:
blockexp(),
blockheader(),
blockopt()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
FIM <- evaluate.fim(poped.db)
dmf <- det(FIM)
blockfinal(fn="",fmf=FIM,
dmf=dmf,
groupsize=poped.db$design$groupsize,
ni=poped.db$design$ni,
xt=poped.db$design$xt,
x=poped.db$design$x,a=poped.db$design$a,
model_switch=poped.db$design$model_switch,
poped.db$parameters$param.pt.val$bpop,
poped.db$parameters$param.pt.val$d,
poped.db$parameters$docc,
poped.db$parameters$param.pt.val$sigma,
poped.db,
opt_xt=TRUE,
fmf_init=FIM,
dmf_init=dmf,
param_cvs_init=get_rse(FIM,poped.db))
Header function for optimization routines
Description
Create some output to the screen and a text file that summarizes the problem you are tying to solve.
Usage
blockheader(
poped.db,
name = "Default",
iter = NULL,
e_flag = !(poped.db$settings$d_switch),
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
fmf = 0,
dmf = 0,
bpop = NULL,
d = NULL,
docc = NULL,
sigma = NULL,
name_header = poped.db$settings$strOutputFileName,
file_path = poped.db$settings$strOutputFilePath,
out_file = NULL,
compute_inv = TRUE,
trflag = TRUE,
header_flag = TRUE,
...
)
Arguments
poped.db |
A PopED database. |
name |
The name used for the output file. Combined with |
iter |
The last number in the name printed to the output file, combined with |
e_flag |
Should output be with uncertainty around parameters? |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
name_header |
The initial portion of the file name. |
file_path |
The path to where the file should be created. |
out_file |
Which file should the output be directed to? A string, a file handle using
|
compute_inv |
should the inverse of the FIM be used to compute expected RSE values? Often not needed except for diagnostic purposes. |
trflag |
Should the optimization be output to the screen and to a file? |
header_flag |
Should the header text be printed out? |
... |
Additional arguments passed to further functions. |
Value
fn A file handle (or '' if name='')
See Also
Other Helper:
blockexp(),
blockfinal(),
blockopt()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
FIM <- evaluate.fim(poped.db)
dmf <- det(FIM)
blockheader(poped.db,name="")
blockheader(name="",iter=1,poped.db)
blockheader(name='',
iter=1,
poped.db,
e_flag=FALSE,
opt_xt=TRUE,
opt_a=TRUE,opt_x=poped.db$settings$optsw[4],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
fmf=FIM,dmf=dmf,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
docc=poped.db$parameters$docc,sigma=poped.db$parameters$param.pt.val$sigma)
blockheader(name='',
iter=1,
poped.db,
e_flag=TRUE,
opt_xt=TRUE,
opt_a=TRUE,opt_x=poped.db$settings$optsw[4],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
fmf=FIM,dmf=dmf,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
docc=poped.db$parameters$docc,sigma=poped.db$parameters$param.pt.val$sigma)
poped.db.1 <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=rbind(c( 0.5,1,2,6,24,36,72,120),
c( 0.5,1.1,2,6,24,36,72,120)),
minxt=rbind(c(0,1,1.5,3,20,30,70,118),
c(0.1,1.1,1.6,3.1,20.1,30.1,70.1,118.1)),
maxxt=c(12,13,14,15,26,44,78,120),
a=70,
mina=0,
maxa=100)
blockheader(poped.db.1,name="",trflag=2,opt_xt=TRUE)
Summarize your optimization settings for optimization routines
Description
Create some output to the screen and a text file that summarizes the optimization settings you will use to optimize.
Usage
blockopt(fn, poped.db, opt_method = "")
Arguments
fn |
The file handle to write to. |
poped.db |
A PopED database. |
opt_method |
If "RS" (random search), "SG" (stochastic gradient) or "DO" (discrete optimization) then specific output is produced. |
See Also
Other Helper:
blockexp(),
blockfinal(),
blockheader()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
blockopt(fn="",poped.db,opt_method="SG")
blockopt(fn="",poped.db,opt_method="RS")
blockopt(fn="",poped.db,opt_method="DO")
Build PopED parameter function from a model function
Description
Build PopED parameter function from a model function
Usage
build_sfg(
model = "ff.PK.1.comp.oral.sd.CL",
covariates = c("dose", "tau"),
par_names = NULL,
etas = "exp",
no_etas = c("F", "Favail"),
env = parent.frame()
)
Arguments
model |
A string of text describing the model function name |
covariates |
A list of covariate names to be filtered out of the model |
par_names |
A list of parameter names in the model file. If not supplied then all undefined variables in the model file are extracted and the covariate names are filtered out of that list. |
etas |
Can be "exp", "prop", "add" or "none". Either one value for all parameters or a list defining the model per parameter. |
no_etas |
Parameters that should not have etas associated with them. |
env |
The environment to create the function in. |
Value
A parameter model function to be used as input to PopED calculations.
Examples
build_sfg(model="ff.PK.1.comp.oral.md.CL")
etas <- c(Favail="exp",KA="exp",V="add",CL="exp")
build_sfg(model="ff.PK.1.comp.oral.md.CL",etas = etas)
Compute the autofocus portion of the stochastic gradient routine
Description
Compute the autofocus portion of the stochastic gradient routine
Usage
calc_autofocus(
m,
ni_var,
dmf,
varopt,
varopto,
maxvar,
minvar,
gradvar,
normgvar,
avar,
model_switch,
groupsize,
xtopt,
xopt,
aopt,
ni,
bpop,
d,
sigma,
docc,
poped.db
)
Arguments
m |
Number of groups in the study. Each individual in a group will have the same design. |
ni_var |
The ni_var. |
dmf |
The initial OFV. If set to zero then it is computed. |
varopt |
The varopt. |
varopto |
The varopto. |
maxvar |
The maxvar. |
minvar |
The minvar. |
gradvar |
The gradvar. |
normgvar |
The normgvar. |
avar |
The avar. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
groupsize |
A vector of the number of individuals in each group. |
xtopt |
The optimal sampling times matrix. |
xopt |
The optimal discrete design variables matrix. |
aopt |
The optimal continuous design variables matrix. |
ni |
A vector of the number of samples in each group. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
poped.db |
A PopED database. |
Value
A list containing:
navar |
The autofocus parameter. |
poped.db |
PopED database. |
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Not run:
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 1,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0,
sgit=20)
## End(Not run)
Calculate the Fisher Information Matrix (FIM) and the OFV(FIM) for either point values or parameters or distributions.
Description
This function computes the expectation of the FIM and OFV(FIM) for either point values of parameter estimates or parameter distributions given the model, parameters, distributions of parameter uncertainty, design and methods defined in the PopED database.
Usage
calc_ofv_and_fim(
poped.db,
ofv = 0,
fim = 0,
d_switch = poped.db$settings$d_switch,
bpopdescr = poped.db$parameters$bpop,
ddescr = poped.db$parameters$d,
bpop = bpopdescr[, 2, drop = F],
d = getfulld(ddescr[, 2, drop = F], poped.db$parameters$covd),
docc_full = getfulld(poped.db$parameters$docc[, 2, drop = F],
poped.db$parameters$covdocc),
model_switch = poped.db$design$model_switch,
ni = poped.db$design$ni,
xt = poped.db$design$xt,
x = poped.db$design$x,
a = poped.db$design$a,
fim.calc.type = poped.db$settings$iFIMCalculationType,
use_laplace = poped.db$settings$iEDCalculationType,
laplace.fim = FALSE,
ofv_fun = poped.db$settings$ofv_fun,
evaluate_fim = TRUE,
...
)
Arguments
poped.db |
A PopED database. |
ofv |
The current ofv. If other than zero then this value is simply returned unchanged. |
fim |
The current FIM. If other than zero then this value is simply returned unchanged. |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
docc_full |
A between occasion variability matrix. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
laplace.fim |
Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done using the standard MC integration technique, so can be slow. |
ofv_fun |
User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it. |
evaluate_fim |
Should the FIM be calculated? |
... |
Other arguments passed to the function. |
Value
A list containing the FIM and OFV(FIM) or the E(FIM) and E(OFV(FIM)) according to the function arguments.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Other E-family:
ed_laplace_ofv(),
ed_mftot(),
evaluate.e.ofv.fim()
Other evaluate_FIM:
evaluate.e.ofv.fim(),
evaluate.fim(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samoples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
calc_ofv_and_fim(poped.db)
## Not run:
calc_ofv_and_fim(poped.db,d_switch=0)
calc_ofv_and_fim(poped.db,d_switch=0,use_laplace=TRUE)
calc_ofv_and_fim(poped.db,d_switch=0,use_laplace=TRUE,laplace.fim=TRUE)
## End(Not run)
Compute an objective function and gradient
Description
Compute an objective function and gradient with respect to the optimization parameters.
This function can be passed to the Broyden Fletcher Goldfarb Shanno (BFGS)
method for nonlinear minimization with box constraints implemented in bfgsb_min.
Usage
calc_ofv_and_grad(
x,
optxt,
opta,
model_switch,
aa,
axt,
groupsize,
ni,
xtopto,
xopto,
aopto,
bpop,
d,
sigma,
docc_full,
poped.db,
only_fim = FALSE
)
Arguments
x |
A matrix for the discrete design variables. Each row is a group. |
optxt |
If sampling times are optimized |
opta |
If continuous design variables are optimized |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
aa |
The aa value |
axt |
the axt value |
groupsize |
A vector of the number of individuals in each group. |
ni |
A vector of the number of samples in each group. |
xtopto |
the xtopto value |
xopto |
the xopto value |
aopto |
the aopto value |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc_full |
A between occasion variability matrix. |
poped.db |
A PopED database. |
only_fim |
Should the gradient be calculated? |
Value
A list containing:
f |
The objective function. |
g |
The gradient. |
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
opta=TRUE
aa=opta*poped.db$settings$cfaa*matrix(1,poped.db$design$m,size(poped.db$design$a,2))
aa
optxt=TRUE
axt=optxt*poped.db$settings$cfaxt*matrix(1,poped.db$design$m,max(poped.db$design_space$maxni))
axt
calc_ofv_and_grad(x=c(poped.db$design$xt,poped.db$design$a),
optxt=optxt, opta=opta,
model_switch=poped.db$design$model_switch,
aa=aa,
axt=axt,
groupsize=poped.db$design$groupsize,
ni=poped.db$design$ni,
xtopto=poped.db$design$xt,
xopto=poped.db$design$x,
aopto=poped.db$design$a,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
sigma=poped.db$parameters$param.pt.val$sigma,
docc_full=poped.db$parameters$param.pt.val$docc,
poped.db,
only_fim=FALSE)
## Not run:
# BFGS search, DOSE and sample time optimization
bfgs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 1,
bUseLineSearch = 0)
## End(Not run)
Create a cell array (a matrix of lists)
Description
Create a cell array as in MATLAB.
Usage
cell(...)
Arguments
... |
Dimensions for the cell array. |
Value
A list of empty lists.
Note
This is a modified version of the same function in the matlab R-package.
See Also
Other MATLAB:
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
cell(3)
cell(2,3)
## define possible values of 2 categorical design variable
x.space <- cell(1,2)
x.space[1,1] <- list(seq(10,100,10))
x.space[1,2] <- list(seq(10,300,10))
x.space
x.space[1,1]
x.space[1,2]
Create global variables in the PopED database
Description
Function takes design variables from input files
and converts them to the global variables needed
in PopED. Typically not used by the user. Instead
use the function create.poped.database.
Usage
convert_variables(poped.db)
Arguments
poped.db |
A PopED database |
Value
A PopED database
See Also
Other poped_input:
create.poped.database(),
create_design(),
create_design_space(),
downsizing_general_design(),
poped.choose()
Create a PopED database
Description
This function takes the input file (a previously created poped database) supplied by the user, or function arguments, and creates a database that can then be used to run all other PopED functions. The function supplies default values to elements of the database that are not specified in the input file or as function arguments. Default arguments are supplied in the Usage section (easiest to use a text search to find values you are interested in).
Usage
create.poped.database(
popedInput = list(),
ff_file = NULL,
ff_fun = poped.choose(popedInput$model$ff_pointer, NULL),
fg_file = NULL,
fg_fun = poped.choose(popedInput$model$fg_pointer, NULL),
fError_file = NULL,
fError_fun = poped.choose(popedInput$model$ferror_pointer, NULL),
optsw = poped.choose(popedInput$settings$optsw, cbind(0, 0, 0, 0, 0)),
xt = poped.choose(popedInput$design[["xt"]], stop("'xt' needs to be defined")),
m = poped.choose(popedInput$design[["m"]], NULL),
x = poped.choose(popedInput$design[["x"]], NULL),
nx = poped.choose(popedInput$design$nx, NULL),
a = poped.choose(popedInput$design[["a"]], NULL),
groupsize = poped.choose(popedInput$design$groupsize,
stop("'groupsize' needs to be defined")),
ni = poped.choose(popedInput$design$ni, NULL),
model_switch = poped.choose(popedInput$design$model_switch, NULL),
maxni = poped.choose(popedInput$design_space$maxni, NULL),
minni = poped.choose(popedInput$design_space$minni, NULL),
maxtotni = poped.choose(popedInput$design_space$maxtotni, NULL),
mintotni = poped.choose(popedInput$design_space$mintotni, NULL),
maxgroupsize = poped.choose(popedInput$design_space$maxgroupsize, NULL),
mingroupsize = poped.choose(popedInput$design_space$mingroupsize, NULL),
maxtotgroupsize = poped.choose(popedInput$design_space$maxtotgroupsize, NULL),
mintotgroupsize = poped.choose(popedInput$design_space$mintotgroupsize, NULL),
maxxt = poped.choose(popedInput$design_space$maxxt, NULL),
minxt = poped.choose(popedInput$design_space$minxt, NULL),
discrete_xt = poped.choose(popedInput$design_space$xt_space, NULL),
discrete_x = poped.choose(popedInput$design_space$discrete_x, NULL),
maxa = poped.choose(popedInput$design_space$maxa, NULL),
mina = poped.choose(popedInput$design_space$mina, NULL),
discrete_a = poped.choose(popedInput$design_space$a_space, NULL),
bUseGrouped_xt = poped.choose(popedInput$design_space$bUseGrouped_xt, FALSE),
G_xt = poped.choose(popedInput$design_space$G_xt, NULL),
bUseGrouped_a = poped.choose(popedInput$design_space$bUseGrouped_a, FALSE),
G_a = poped.choose(popedInput$design_space$G_a, NULL),
bUseGrouped_x = poped.choose(popedInput$design_space$bUseGrouped_x, FALSE),
G_x = poped.choose(popedInput$design_space[["G_x"]], NULL),
iFIMCalculationType = poped.choose(popedInput$settings$iFIMCalculationType, 1),
iApproximationMethod = poped.choose(popedInput$settings$iApproximationMethod, 0),
iFOCENumInd = poped.choose(popedInput$settings$iFOCENumInd, 1000),
prior_fim = poped.choose(popedInput$settings$prior_fim, matrix(0, 0, 1)),
strAutoCorrelationFile = poped.choose(popedInput$model$auto_pointer, ""),
d_switch = poped.choose(popedInput$settings$d_switch, 1),
ofv_calc_type = poped.choose(popedInput$settings$ofv_calc_type, 4),
ds_index = popedInput$parameters$ds_index,
strEDPenaltyFile = poped.choose(popedInput$settings$strEDPenaltyFile, ""),
ofv_fun = poped.choose(popedInput$settings$ofv_fun, NULL),
iEDCalculationType = poped.choose(popedInput$settings$iEDCalculationType, 0),
ED_samp_size = poped.choose(popedInput$settings$ED_samp_size, 45),
bLHS = poped.choose(popedInput$settings$bLHS, 1),
strUserDistributionFile = poped.choose(popedInput$model$user_distribution_pointer, ""),
nbpop = popedInput$parameters$nbpop,
NumRanEff = popedInput$parameters$NumRanEff,
NumDocc = popedInput$parameters$NumDocc,
NumOcc = popedInput$parameters$NumOcc,
bpop = poped.choose(popedInput$parameters$bpop, stop("bpop must be defined")),
d = poped.choose(popedInput$parameters$d, NULL),
covd = popedInput$parameters$covd,
sigma = popedInput$parameters$sigma,
docc = poped.choose(popedInput$parameters$docc, matrix(0, 0, 3)),
covdocc = poped.choose(popedInput$parameters$covdocc, zeros(1, length(docc[, 2, drop =
F]) * (length(docc[, 2, drop = F]) - 1)/2)),
notfixed_bpop = popedInput$parameters$notfixed_bpop,
notfixed_d = popedInput$parameters$notfixed_d,
notfixed_covd = popedInput$parameters$notfixed_covd,
notfixed_docc = popedInput$parameters$notfixed_docc,
notfixed_covdocc = poped.choose(popedInput$parameters$notfixed_covdocc, zeros(1,
length(covdocc))),
notfixed_sigma = poped.choose(popedInput$parameters$notfixed_sigma, t(rep(1,
size(sigma, 2)))),
notfixed_covsigma = poped.choose(popedInput$parameters$notfixed_covsigma, zeros(1,
length(notfixed_sigma) * (length(notfixed_sigma) - 1)/2)),
reorder_parameter_vectors = FALSE,
bUseRandomSearch = poped.choose(popedInput$settings$bUseRandomSearch, TRUE),
bUseStochasticGradient = poped.choose(popedInput$settings$bUseStochasticGradient, TRUE),
bUseLineSearch = poped.choose(popedInput$settings$bUseLineSearch, TRUE),
bUseExchangeAlgorithm = poped.choose(popedInput$settings$bUseExchangeAlgorithm, FALSE),
bUseBFGSMinimizer = poped.choose(popedInput$settings$bUseBFGSMinimizer, FALSE),
EACriteria = poped.choose(popedInput$settings$EACriteria, 1),
strRunFile = poped.choose(popedInput$settings$run_file_pointer, ""),
poped_version = poped.choose(popedInput$settings$poped_version,
packageVersion("PopED")),
modtit = poped.choose(popedInput$settings$modtit, "PopED model"),
output_file = poped.choose(popedInput$settings$output_file, paste("PopED_output",
"_summary", sep = "")),
output_function_file = poped.choose(popedInput$settings$output_function_file,
paste("PopED", "_output_", sep = "")),
strIterationFileName = poped.choose(popedInput$settings$strIterationFileName,
paste("PopED", "_current.R", sep = "")),
user_data = poped.choose(popedInput$settings$user_data, cell(0, 0)),
ourzero = poped.choose(popedInput$settings$ourzero, 1e-05),
dSeed = poped.choose(popedInput$settings$dSeed, NULL),
line_opta = poped.choose(popedInput$settings$line_opta, NULL),
line_optx = poped.choose(popedInput$settings$line_optx, NULL),
bShowGraphs = poped.choose(popedInput$settings$bShowGraphs, FALSE),
use_logfile = poped.choose(popedInput$settings$use_logfile, FALSE),
m1_switch = poped.choose(popedInput$settings$m1_switch, 1),
m2_switch = poped.choose(popedInput$settings$m2_switch, 1),
hle_switch = poped.choose(popedInput$settings$hle_switch, 1),
gradff_switch = poped.choose(popedInput$settings$gradff_switch, 1),
gradfg_switch = poped.choose(popedInput$settings$gradfg_switch, 1),
grad_all_switch = poped.choose(popedInput$settings$grad_all_switch, 1),
rsit_output = poped.choose(popedInput$settings$rsit_output, 5),
sgit_output = poped.choose(popedInput$settings$sgit_output, 1),
hm1 = poped.choose(popedInput$settings[["hm1"]], 1e-05),
hlf = poped.choose(popedInput$settings[["hlf"]], 1e-05),
hlg = poped.choose(popedInput$settings[["hlg"]], 1e-05),
hm2 = poped.choose(popedInput$settings[["hm2"]], 1e-05),
hgd = poped.choose(popedInput$settings[["hgd"]], 1e-05),
hle = poped.choose(popedInput$settings[["hle"]], 1e-05),
AbsTol = poped.choose(popedInput$settings$AbsTol, 1e-06),
RelTol = poped.choose(popedInput$settings$RelTol, 1e-06),
iDiffSolverMethod = poped.choose(popedInput$settings$iDiffSolverMethod, NULL),
bUseMemorySolver = poped.choose(popedInput$settings$bUseMemorySolver, FALSE),
rsit = poped.choose(popedInput$settings[["rsit"]], 300),
sgit = poped.choose(popedInput$settings[["sgit"]], 150),
intrsit = poped.choose(popedInput$settings$intrsit, 250),
intsgit = poped.choose(popedInput$settings$intsgit, 50),
maxrsnullit = poped.choose(popedInput$settings$maxrsnullit, 50),
convergence_eps = poped.choose(popedInput$settings$convergence_eps, 1e-08),
rslxt = poped.choose(popedInput$settings$rslxt, 10),
rsla = poped.choose(popedInput$settings$rsla, 10),
cfaxt = poped.choose(popedInput$settings$cfaxt, 0.001),
cfaa = poped.choose(popedInput$settings$cfaa, 0.001),
bGreedyGroupOpt = poped.choose(popedInput$settings$bGreedyGroupOpt, FALSE),
EAStepSize = poped.choose(popedInput$settings$EAStepSize, 0.01),
EANumPoints = poped.choose(popedInput$settings$EANumPoints, FALSE),
EAConvergenceCriteria = poped.choose(popedInput$settings$EAConvergenceCriteria, 1e-20),
bEANoReplicates = poped.choose(popedInput$settings$bEANoReplicates, FALSE),
BFGSConvergenceCriteriaMinStep = NULL,
BFGSProjectedGradientTol = poped.choose(popedInput$settings$BFGSProjectedGradientTol,
1e-04),
BFGSTolerancef = poped.choose(popedInput$settings$BFGSTolerancef, 0.001),
BFGSToleranceg = poped.choose(popedInput$settings$BFGSToleranceg, 0.9),
BFGSTolerancex = poped.choose(popedInput$settings$BFGSTolerancex, 0.1),
ED_diff_it = poped.choose(popedInput$settings$ED_diff_it, 30),
ED_diff_percent = poped.choose(popedInput$settings$ED_diff_percent, 10),
line_search_it = poped.choose(popedInput$settings$ls_step_size, 50),
Doptim_iter = poped.choose(popedInput$settings$iNumSearchIterationsIfNotLineSearch, 1),
iCompileOption = poped.choose(popedInput$settings$parallel$iCompileOption, -1),
iUseParallelMethod = poped.choose(popedInput$settings$parallel$iUseParallelMethod, 1),
MCC_Dep = NULL,
strExecuteName = poped.choose(popedInput$settings$parallel$strExecuteName,
"calc_fim.exe"),
iNumProcesses = poped.choose(popedInput$settings$parallel$iNumProcesses, 2),
iNumChunkDesignEvals = poped.choose(popedInput$settings$parallel$iNumChunkDesignEvals,
-2),
Mat_Out_Pre = poped.choose(popedInput$settings$parallel$strMatFileOutputPrefix,
"parallel_output"),
strExtraRunOptions = poped.choose(popedInput$settings$parallel$strExtraRunOptions, ""),
dPollResultTime = poped.choose(popedInput$settings$parallel$dPollResultTime, 0.1),
strFunctionInputName = poped.choose(popedInput$settings$parallel$strFunctionInputName,
"function_input"),
bParallelRS = poped.choose(popedInput$settings$parallel$bParallelRS, FALSE),
bParallelSG = poped.choose(popedInput$settings$parallel$bParallelSG, FALSE),
bParallelMFEA = poped.choose(popedInput$settings$parallel$bParallelMFEA, FALSE),
bParallelLS = poped.choose(popedInput$settings$parallel$bParallelLS, FALSE)
)
Arguments
popedInput |
A PopED database file or an empty list |
ff_file |
A string giving the function name or filename and path of the structural model.
The filename and the function name must be the same if giving a filename.
e.g. |
ff_fun |
Function describing the structural model. e.g. |
fg_file |
A string giving the function name or filename and path of the
parameter model.
The filename and the function name must be the same if giving a filename.
e.g. |
fg_fun |
Function describing the parameter model. e.g. |
fError_file |
A string giving the function name or filename and path of the
residual error model.
The filename and the function name must be the same if giving a filename.
e.g. |
fError_fun |
Function describing the residual error model. e.g. |
optsw |
Row vector of optimization tasks (1=TRUE,0=FALSE) in the following order: (Samples per subject, Sampling schedule, Discrete design variable, Continuous design variable, Number of id per group). All elements set to zero => only calculate the FIM with current design |
xt |
Matrix defining the initial sampling schedule.
Each row is a group/individual.
If only one vector is supplied, e.g. |
m |
Number of groups in the study. Each individual in a group will have the same design. |
x |
A matrix defining the initial discrete values for the model Each row is a group/individual. |
nx |
Number of discrete design variables. |
a |
Matrix defining the initial continuous covariate values. n_rows=number of groups, n_cols=number of covariates. If the number of rows is one and the number of groups > 1 then all groups are assigned the same values. |
groupsize |
Vector defining the size of the different groups (num individuals in each group). If only one number then the number will be the same in every group. |
ni |
Vector defining the number of samples for each group. |
model_switch |
Matrix defining which response a certain sampling time belongs to. |
maxni |
Max number of samples per group/individual |
minni |
Min number of samples per group/individual |
maxtotni |
Number defining the maximum number of samples allowed in the experiment. |
mintotni |
Number defining the minimum number of samples allowed in the experiment. |
maxgroupsize |
Vector defining the max size of the different groups (max number of individuals in each group) |
mingroupsize |
Vector defining the min size of the different groups (min num individuals in each group) – |
maxtotgroupsize |
The total maximal groupsize over all groups |
mintotgroupsize |
The total minimal groupsize over all groups |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
discrete_xt |
Cell array |
discrete_x |
Cell array defining the discrete variables for each x value.
See examples in |
maxa |
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value |
mina |
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value |
discrete_a |
Cell array |
bUseGrouped_xt |
Use grouped time points (1=TRUE, 0=FALSE). |
G_xt |
Matrix defining the grouping of sample points. Matching integers mean that the points are matched. |
bUseGrouped_a |
Use grouped covariates (1=TRUE, 0=FALSE) |
G_a |
Matrix defining the grouping of covariates. Matching integers mean that the points are matched. |
bUseGrouped_x |
Use grouped discrete design variables (1=TRUE, 0=FALSE). |
G_x |
Matrix defining the grouping of discrete design variables. Matching integers mean that the points are matched. |
iFIMCalculationType |
Fisher Information Matrix type
|
iApproximationMethod |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI |
iFOCENumInd |
Num individuals in each step of FOCE |
prior_fim |
The prior FIM (added to calculated FIM) |
strAutoCorrelationFile |
Filename and path, or function name, for the Autocorrelation function, empty string means no autocorrelation. |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
ofv_calc_type |
OFV calculation type for FIM
|
ds_index |
Ds_index is a vector set to 1 if a parameter is uninteresting, otherwise 0.
size=(1,num unfixed parameters). First unfixed bpop, then unfixed d, then unfixed docc and last unfixed sigma.
Default is the fixed effects being important, everything else not important. Used in conjunction with
|
strEDPenaltyFile |
Penalty function name or path and filename, empty string means no penalty. User defined criterion can be defined this way. |
ofv_fun |
User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it. |
iEDCalculationType |
ED Integral Calculation, 0=Monte-Carlo-Integration, 1=Laplace Approximation, 2=BFGS Laplace Approximation – – |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
strUserDistributionFile |
Filename and path, or function name, for user defined distributions for E-family designs |
nbpop |
Number of typical values |
NumRanEff |
Number of IIV parameters. Typically can be computed from other values and not supplied. |
NumDocc |
Number of IOV variance parameters. Typically can be computed from other values and not supplied. |
NumOcc |
Number of occasions. Typically can be computed from other values and not supplied. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
covd |
Column major vector defining the covariances of the IIV variances.
That is, from your full IIV matrix |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
covdocc |
Column major vector defining the covariance of the IOV, as in covd. |
notfixed_bpop |
Vector defining if a typical value is fixed or not (1=not fixed, 0=fixed). The parameter order of 'notfixed_bpop' is defined in the 'fg_fun' or 'fg_file'. If you use named arguments in 'notfixed_bpop' then the order of this vector can be rearranged to match the 'fg_fun' or 'fg_file'. See 'reorder_parameter_vectors'. |
notfixed_d |
Vector defining if a IIV is fixed or not (1=not fixed, 0=fixed). The parameter order of 'notfixed_d' is defined in the 'fg_fun' or 'fg_file'. If you use named arguments in 'notfixed_d' then the order of this vector can be rearranged to match the 'fg_fun' or 'fg_file'. See 'reorder_parameter_vectors'. . |
notfixed_covd |
Vector defining if a covariance IIV is fixed or not (1=not fixed, 0=fixed) |
notfixed_docc |
Vector defining if an IOV variance is fixed or not (1=not fixed, 0=fixed) |
notfixed_covdocc |
Vector row major order for lower triangular matrix defining if a covariance IOV is fixed or not (1=not fixed, 0=fixed) |
notfixed_sigma |
Vector defining if a residual error parameter is fixed or not (1=not fixed, 0=fixed) |
notfixed_covsigma |
Vector defining if a covariance residual error parameter is fixed or not (1=not fixed, 0=fixed). Default is fixed. |
reorder_parameter_vectors |
If you use named arguments in 'bpop' or 'd' then PopED will try to figure out the order of the parameters based on what is found in the 'fg_fun'. See the resulting ‘poped_db$parameters' and make sure the order matches with ’fg_fun'. |
bUseRandomSearch |
Use random search (1=TRUE, 0=FALSE) |
bUseStochasticGradient |
Use Stochastic Gradient search (1=TRUE, 0=FALSE) |
bUseLineSearch |
Use Line search (1=TRUE, 0=FALSE) |
bUseExchangeAlgorithm |
Use Exchange algorithm (1=TRUE, 0=FALSE) |
bUseBFGSMinimizer |
Use BFGS Minimizer (1=TRUE, 0=FALSE) |
EACriteria |
Exchange Algorithm Criteria, 1 = Modified, 2 = Fedorov |
strRunFile |
Filename and path, or function name, for a run file that is used instead of the regular PopED call. |
poped_version |
The current PopED version |
modtit |
The model title |
output_file |
Filename and path of the output file during search |
output_function_file |
Filename suffix of the result function file |
strIterationFileName |
Filename and path for storage of current optimal design |
user_data |
User defined data structure that, for example could be used to send in data to the model |
ourzero |
Value to interpret as zero in design |
dSeed |
The seed number used for optimization and sampling – integer or -1 which creates a random seed |
line_opta |
Vector for line search on continuous design variables (1=TRUE,0=FALSE) |
line_optx |
Vector for line search on discrete design variables (1=TRUE,0=FALSE) |
bShowGraphs |
Use graph output during search |
use_logfile |
If a log file should be used (0=FALSE, 1=TRUE) |
m1_switch |
Method used to calculate M1 (0=Complex difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) |
m2_switch |
Method used to calculate M2 (0=Central difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) |
hle_switch |
Method used to calculate linearization of residual error (0=Complex difference, 1=Central difference, 30=Automatic differentiation) |
gradff_switch |
Method used to calculate the gradient of the model (0=Complex difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) |
gradfg_switch |
Method used to calculate the gradient of the parameter vector g (0=Complex difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) |
grad_all_switch |
Method used to calculate all the gradients (0=Complex difference, 1=Central difference) |
rsit_output |
Number of iterations in random search between screen output |
sgit_output |
Number of iterations in stochastic gradient search between screen output |
hm1 |
Step length of derivative of linearized model w.r.t. typical values |
hlf |
Step length of derivative of model w.r.t. g |
hlg |
Step length of derivative of g w.r.t. b |
hm2 |
Step length of derivative of variance w.r.t. typical values |
hgd |
Step length of derivative of OFV w.r.t. time |
hle |
Step length of derivative of model w.r.t. sigma |
AbsTol |
The absolute tolerance for the diff equation solver |
RelTol |
The relative tolerance for the diff equation solver |
iDiffSolverMethod |
The diff equation solver method, NULL as default. |
bUseMemorySolver |
If the differential equation results should be stored in memory (1) or not (0) |
rsit |
Number of Random search iterations |
sgit |
Number of stochastic gradient iterations |
intrsit |
Number of Random search iterations with discrete optimization. |
intsgit |
Number of Stochastic Gradient search iterations with discrete optimization |
maxrsnullit |
Iterations until adaptive narrowing in random search |
convergence_eps |
Stochastic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal) |
rslxt |
Random search locality factor for sample times |
rsla |
Random search locality factor for covariates |
cfaxt |
Stochastic Gradient search first step factor for sample times |
cfaa |
Stochastic Gradient search first step factor for covariates |
bGreedyGroupOpt |
Use greedy algorithm for group assignment optimization |
EAStepSize |
Exchange Algorithm StepSize |
EANumPoints |
Exchange Algorithm NumPoints |
EAConvergenceCriteria |
Exchange Algorithm Convergence Limit/Criteria |
bEANoReplicates |
Avoid replicate samples when using Exchange Algorithm |
BFGSConvergenceCriteriaMinStep |
BFGS Minimizer Convergence Criteria Minimum Step |
BFGSProjectedGradientTol |
BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance |
BFGSTolerancef |
BFGS Minimizer Line Search Tolerance f |
BFGSToleranceg |
BFGS Minimizer Line Search Tolerance g |
BFGSTolerancex |
BFGS Minimizer Line Search Tolerance x |
ED_diff_it |
Number of iterations in ED-optimal design to calculate convergence criteria |
ED_diff_percent |
ED-optimal design convergence criteria in percent |
line_search_it |
Number of grid points in the line search |
Doptim_iter |
Number of iterations of full Random search and full Stochastic Gradient if line search is not used |
iCompileOption |
******START OF PARALLEL OPTIONS********** Compile options for PopED
|
iUseParallelMethod |
Parallel method to use (0 = Matlab PCT, 1 = MPI) |
MCC_Dep |
Additional dependencies used in MCC compilation (mat-files), if several space separated |
strExecuteName |
Compilation output executable name |
iNumProcesses |
Number of processes to use when running in parallel (e.g. 3 = 2 workers, 1 job manager) |
iNumChunkDesignEvals |
Number of design evaluations that should be evaluated in each process before getting new work from job manager |
Mat_Out_Pre |
The prefix of the output mat file to communicate with the executable |
strExtraRunOptions |
Extra options send to e$g. the MPI executable or a batch script, see execute_parallel$m for more information and options |
dPollResultTime |
Polling time to check if the parallel execution is finished |
strFunctionInputName |
The file containing the popedInput structure that should be used to evaluate the designs |
bParallelRS |
If the random search is going to be executed in parallel |
bParallelSG |
If the stochastic gradient search is going to be executed in parallel |
bParallelMFEA |
If the modified exchange algorithm is going to be executed in parallel |
bParallelLS |
If the line search is going to be executed in parallel |
Value
A PopED database
See Also
Other poped_input:
convert_variables(),
create_design(),
create_design_space(),
downsizing_general_design(),
poped.choose()
Examples
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## evaluate initial design
evaluate_design(poped.db)
Create design variables for a full description of a design.
Description
Create design variables to fully describe a design. If variables are supplied then these variables are checked for consistency and, if possible, changed to sizes that make sense if there are inconsistencies. Returns a list of matricies compatible with PopED.
Usage
create_design(
xt,
groupsize,
m = NULL,
x = NULL,
a = NULL,
ni = NULL,
model_switch = NULL
)
Arguments
xt |
Matrix defining the sampling schedule. Each row is a group. |
groupsize |
Vector defining the size of the different groups (number of individuals in each group). |
m |
A number defining the number of groups. Computed from xt if not defined. |
x |
A matrix defining the discrete design variables for the model Each row is a group. |
a |
Matrix defining the continuous design variables. Each row is a group. |
ni |
Vector defining the number of samples for each group, computed as all elements of xt for each group by default. |
model_switch |
Matrix defining which response a certain sampling time belongs to. Defaults to one for all elements of xt. |
Details
If a value (or a vector/list of values) is supplied that corresponds to only one group and the design has multiple groups then all groups will have the same value(s). If a matrix is expected then a list of lists can be supplied instead, each list corresponding to a group.
See Also
Other poped_input:
convert_variables(),
create.poped.database(),
create_design_space(),
downsizing_general_design(),
poped.choose()
Examples
library(PopED)
xt1 <- list(c(1,2,3),c(1,2,3,4))
xt4 <- list(c(1,2,3,4,5),c(1,2,3,4))
xt2 <- rbind(c(1,2,3,4),c(1,2,3,4))
xt3 <- c(1,2,3,4)
design_1 <- create_design(xt=xt1,groupsize=20)
design_2 <- create_design(xt=xt4,groupsize=20)
design_3 <- create_design(xt=xt2,groupsize=20)
design_4 <- create_design(xt=xt3,groupsize=20)
design_5 <- create_design(xt=xt3,groupsize=20,m=3)
design_6 <- create_design(xt=xt1,groupsize=20,model_switch=ones(2,4))
design_7 <-create_design(xt=xt1,groupsize=20,a=c(2,3,4))
design_8 <-create_design(xt=xt1,groupsize=20,a=rbind(c(2,3,4),c(4,5,6)))
design_9 <-create_design(xt=xt1,groupsize=20,a=list(c(2,3,4,6),c(4,5,6)))
design_10 <-create_design(xt=xt1,groupsize=20,a=list(c(2,3,4),c(4,5,6)))
design_11 <-create_design(xt=c(0,1,2,4,6,8,24),
groupsize=50,
a=c(WT=70,DOSE=1000))
design_12 <-create_design(xt=c(0,1,2,4,6,8,24),
groupsize=50,
a=c(WT=70,DOSE=1000),m=2)
design_13 <-create_design(xt=c(0,1,2,4,6,8,24),
groupsize=50,
a=list(c(WT=70,DOSE=1000),c(DOSE=90,WT=200,AGE=45)),m=2)
design_14 <-create_design(xt=c(0,1,2,4,6,8,24),
groupsize=50,
a=list(list(WT=70,DOSE=1000),list(DOSE=90,WT=200,AGE=45)),m=2)
design_15 <-create_design(xt=xt4,
groupsize=c(50,20),
a=rbind(c("DOSE"=2,"WT"=3,"AGE"=4),
c(4,5,6)))
Create design variables and a design space for a full description of an optimization problem.
Description
create_design_space takes an initial design and arguments for a design space and
creates a design and design space for design optimization.
Checks the sizes of supplied design space variables and
changes them to sizes that make sense if there are inconsistencies.
Function arguments can use shorthand notation (single values, vectors, lists of vectors and
list of list) or matricies.
Returns a list of matricies compatible with PopED.
Usage
create_design_space(
design,
maxni = NULL,
minni = NULL,
maxtotni = NULL,
mintotni = NULL,
maxgroupsize = NULL,
mingroupsize = NULL,
maxtotgroupsize = NULL,
mintotgroupsize = NULL,
maxxt = NULL,
minxt = NULL,
xt_space = NULL,
maxa = NULL,
mina = NULL,
a_space = NULL,
x_space = NULL,
use_grouped_xt = FALSE,
grouped_xt = NULL,
use_grouped_a = FALSE,
grouped_a = NULL,
use_grouped_x = FALSE,
grouped_x = NULL,
our_zero = NULL
)
Arguments
design |
The output from a call to |
maxni |
Vector defining the maximum number of samples per group. |
minni |
Vector defining the minimum number of samples per group. |
maxtotni |
Number defining the maximum number of samples allowed in the experiment. |
mintotni |
Number defining the minimum number of samples allowed in the experiment. |
maxgroupsize |
Vector defining the maximum size of the different groups (maximum number of individuals in each group) |
mingroupsize |
Vector defining the minimum size of the different groups (minimum num individuals in each group) |
maxtotgroupsize |
The total maximal groupsize over all groups |
mintotgroupsize |
The total minimal groupsize over all groups |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
xt_space |
Cell array |
maxa |
Vector defining the maximum value for each covariate. IF a single value is supplied then all a values are given the same maximum value |
mina |
Vector defining the minimum value for each covariate. IF a single value is supplied then all a values are given the same minimum value |
a_space |
Cell array |
x_space |
Cell array |
use_grouped_xt |
Group sampling times between groups so that each group has the same values ( |
grouped_xt |
Matrix defining the grouping of sample points. Matching integers mean that the points are matched.
Allows for finer control than |
use_grouped_a |
Group continuous design variables between groups so that each group has the same values ( |
grouped_a |
Matrix defining the grouping of continuous design variables. Matching integers mean that the values are matched.
Allows for finer control than |
use_grouped_x |
Group discrete design variables between groups so that each group has the same values ( |
grouped_x |
Matrix defining the grouping of discrete design variables. Matching integers mean that the values are matched.
Allows for finer control than |
our_zero |
Value to interpret as zero in design. |
Details
If a value (or a vector or a list of values) is supplied that corresponds to only one group and the design has multiple groups then all groups will have the same value(s). If a matrix is expected then a list of lists can be supplied instead, each list corresponding to a group.
See Also
Other poped_input:
convert_variables(),
create.poped.database(),
create_design(),
downsizing_general_design(),
poped.choose()
Examples
library(PopED)
design_1 <- create_design(xt=list(c(1,2,3,4,5),
c(1,2,3,4)),
groupsize=c(50,20),
a=list(c(WT=70,DOSE=1000),
c(DOSE=1000,WT=35)))
ds_1 <- create_design_space(design_1)
ds_1_a <- create_design_space(design_1,our_zero = 1e-5)
ds_2 <- create_design_space(design_1,maxni=10,maxxt=10,minxt=0)
ds_3 <- create_design_space(design_1,maxni=10,mingroupsize=20,maxxt=10,minxt=0)
ds_4 <- create_design_space(design_1,maxa=c(100,2000))
ds_5 <- create_design_space(design_1,mina=c(10,20))
design_2 <- create_design(xt=list(c(1,2,3,4,5),
c(1,2,3,4)),
groupsize=c(50,20),
a=list(c(WT=70,DOSE=1000),
c(WT=35,DOSE=1000)),
x=list(c(SEX=1,DOSE_discrete=100),
c(SEX=2,DOSE_discrete=200)))
ds_6 <- create_design_space(design_2)
ds_7 <- create_design_space(design_2,
x_space=list(SEX=c(1,2),
DOSE_discrete=seq(100,400,by=20)))
ds_8 <- create_design_space(design_2,
x_space=list(SEX=c(1,2),
DOSE_discrete=seq(100,400,by=20)),
grouped_xt=c(1,2,3,4,5))
ds_9 <- create_design_space(design_2,
x_space=list(SEX=c(1,2),
DOSE_discrete=seq(100,400,by=20)),
use_grouped_xt=TRUE)
design_3 <- create_design(xt=list(c(1,2,3,4,5),
c(1,2,3,4)),
groupsize=c(50,20),
a=list(c(WT=35,DOSE=1000)),
x=list(c(SEX=1,DOSE_discrete=100)))
ds_10 <- create_design_space(design_3,
x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
use_grouped_a=TRUE)
ds_11 <- create_design_space(design_2,
x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
grouped_a=list(c(1,2),c(3,2)))
ds_12 <- create_design_space(design_3,
x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
use_grouped_x=TRUE)
ds_13 <- create_design_space(design_3,
x_space=list(SEX=c(1,2),DOSE_discrete=seq(100,400,by=20)),
grouped_x=list(c(1,2),c(3,2)))
seq_1 <- 1:10
ds_14 <- create_design_space(design_1,maxxt=10,minxt=0,
xt_space = list(seq_1,seq_1,seq_1,seq_1,seq_1))
ds_15 <- create_design_space(design_1,maxxt=10,minxt=0,xt_space = list(seq_1))
possible_values <- as.matrix(cbind(list(0:10),list(0:10),list(0:10),list(0:20),list(0:20)))
xt_space <- as.matrix(rbind(possible_values,possible_values))
ds_16 <- create_design_space(design_1,maxxt=10,minxt=0,xt_space = xt_space)
ds_17 <- create_design_space(design_1,a_space = list(1:100,seq(1000,100000,by=1000)))
Display a summary of output from poped_db
Description
Display a summary of output from poped_db
Usage
design_summary(poped_db, file = "", ...)
Arguments
poped_db |
An object returned from |
file |
A file handle to write to. Default is to the R console. |
... |
Additional arguments. Passed to |
Function written to match MATLAB's diag function
Description
There are some differences between tha MATLAB and the R version of diag.
Specifically, if a 1xN or a Nx1 matrix is supplied to the R
diag function then just the first element of this vector is
returned. This function tries to match the MATLAB version in handling vectors
(matricies with one dimension equal to one), and will return a diagonal
matrix in these situations.
Usage
diag_matlab(mat)
Arguments
mat |
Either a vector to make into a diagonal matrix or a matrix you want to extract the diagonal from |
Value
Either a diagonal matrix or the diagonal of a matrix.
See Also
Other MATLAB:
cell(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
diag_matlab(3)
diag_matlab(c(1,2,3))
diag_matlab(cbind(1,2,3))
diag_matlab(rbind(1,2,3))
diag_matlab(matrix(c(1, 2, 3),6,6))
# here is where the R default does something different
diag(cbind(1,2,3))
diag(rbind(1,2,3))
Downsize a general design to a specific design
Description
Function takes a design with potentially empty design
variables and rescues the design so that a FIM can be calculated using mftot.
Usage
downsizing_general_design(poped.db)
Arguments
poped.db |
A PopED database |
Value
A list containing:
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
A matrix of fixed effect parameter values. |
See Also
Other poped_input:
convert_variables(),
create.poped.database(),
create_design(),
create_design_space(),
poped.choose()
Evaluate the expectation of determinant the Fisher Information Matrix (FIM) using the Laplace approximation.
Description
Compute the expectation of the det(FIM) using the Laplace
approximation to the expectation. Computations are made based on the model,
parameters, distributions of parameter uncertainty, design and methods
defined in the PopED database or as arguments to the function.
Usage
ed_laplace_ofv(
model_switch,
groupsize,
ni,
xtopto,
xopto,
aopto,
bpopdescr,
ddescr,
covd,
sigma,
docc,
poped.db,
method = 1,
return_gradient = FALSE,
optxt = poped.db$settings$optsw[2],
opta = poped.db$settings$optsw[4],
x = c(),
...
)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
groupsize |
A vector of the number of individuals in each group. |
ni |
A vector of the number of samples in each group. |
xtopto |
the sampling times |
xopto |
the discrete design variables |
aopto |
the continuous design variables |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
covd |
Column major vector defining the covariances of the IIV variances.
That is, from your full IIV matrix |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
poped.db |
A PopED database. |
method |
If 0 then use an optimization routine translated from PopED code written in MATLAB to
optimize the parameters in the Laplace approximation. If 1 then use |
return_gradient |
Should the gradient be returned. |
optxt |
If sampling times are optimized |
opta |
If continuous design variables are optimized |
x |
The design parameters to compute the gradient on. |
... |
Arguments passed through from other functions, does not pass anything to another function. |
Details
This computation follows the method outlined in Dodds et al, "Robust Population Pharmacokinetic Experiment Design" JPP, 2005, equation 16.
Typically this function will not be run by the user. Instead use evaluate.e.ofv.fim.
Value
The FIM and the hessian of the FIM.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Other E-family:
calc_ofv_and_fim(),
ed_mftot(),
evaluate.e.ofv.fim()
Examples
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error to
## avoid sample times at very low concentrations (time 0 or very late samoples).
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
######################
# Normal distribution
######################
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_n <- cbind(ones(length(bpop_vals),1)*1, # normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_n["Favail",] <- c(0,1,0)
bpop_vals_ed_n
## -- Define initial design and design space
poped.db.n <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_n,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
## ED evaluate using LaPlace approximation
tic(); output <- evaluate.e.ofv.fim(poped.db.n,use_laplace=TRUE); toc()
output$E_ofv
## Not run:
## ED value using MC integration (roughly)
tic();e_ofv_mc_n <- evaluate.e.ofv.fim(poped.db.n,ED_samp_size=500,ofv_calc_type = 1);toc()
e_ofv_mc_n$E_ofv
## Using ed_laplce_ofv directly
ed_laplace_ofv(model_switch=poped.db.n$design$model_switch,
groupsize=poped.db.n$design$groupsize,
ni=poped.db.n$design$ni,
xtopto=poped.db.n$design$xt,
xopto=poped.db.n$design$x,
aopto=poped.db.n$design$a,
bpopdescr=poped.db.n$parameters$bpop,
ddescr=poped.db.n$parameters$d,
covd=poped.db.n$parameters$covd,
sigma=poped.db.n$parameters$sigma,
docc=poped.db.n$parameters$docc,
poped.db.n)
######################
# Log-normal distribution
######################
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db.ln <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
## ED evaluate using LaPlace approximation
tic()
output <- evaluate.e.ofv.fim(poped.db.ln,use_laplace=TRUE)
toc()
output$E_ofv
## expected value (roughly)
tic()
e_ofv_mc_ln <- evaluate.e.ofv.fim(poped.db.ln,ED_samp_size=500,ofv_calc_type = 1)[["E_ofv"]]
toc()
e_ofv_mc_ln
## Using ed_laplce_ofv directly
ed_laplace_ofv(model_switch=poped.db.ln$design$model_switch,
groupsize=poped.db.ln$design$groupsize,
ni=poped.db.ln$design$ni,
xtopto=poped.db.ln$design$xt,
xopto=poped.db.ln$design$x,
aopto=poped.db.ln$design$a,
bpopdescr=poped.db.ln$parameters$bpop,
ddescr=poped.db.ln$parameters$d,
covd=poped.db.ln$parameters$covd,
sigma=poped.db.ln$parameters$sigma,
docc=poped.db.ln$parameters$docc,
poped.db.ln)
## End(Not run)
Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM).
Description
Compute the expectation of the FIM given the model, parameters, distributions of parameter uncertainty, design and methods defined in the PopED database.
Usage
ed_mftot(
model_switch,
groupsize,
ni,
xtoptn,
xoptn,
aoptn,
bpopdescr,
ddescr,
covd,
sigma,
docc,
poped.db,
calc_fim = TRUE,
...
)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
groupsize |
A vector of the number of individuals in each group. |
ni |
A vector of the number of samples in each group. |
xtoptn |
The xtoptn value |
xoptn |
The xoptn |
aoptn |
The aoptn value |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
covd |
Column major vector defining the covariances of the IIV variances.
That is, from your full IIV matrix |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
poped.db |
A PopED database. |
calc_fim |
Should the FIM be calculated or should we just use the user defined ed_penalty_pointer. |
... |
Other arguments passed to the function. |
Value
A list containing the E(FIM) and E(OFV(FIM)) and the a poped.db.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Other E-family:
calc_ofv_and_fim(),
ed_laplace_ofv(),
evaluate.e.ofv.fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samoples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
# very few samples
poped.db$settings$ED_samp_size=10
ed_mftot(model_switch=poped.db$design$model_switch,
groupsize=poped.db$design$groupsize,
ni=poped.db$design$ni,
xtoptn=poped.db$design$xt,
xoptn=poped.db$design$x,
aoptn=poped.db$design$a,
bpopdescr=poped.db$parameters$bpop,
ddescr=poped.db$parameters$d,
covd=poped.db$parameters$covd,
sigma=poped.db$parameters$sigma,
docc=poped.db$parameters$docc,
poped.db)["ED_ofv"]
Compute efficiency.
Description
Efficiency calculation between two designs.
Usage
efficiency(
ofv_init,
ofv_final,
poped_db,
npar = get_fim_size(poped_db),
ofv_calc_type = poped_db$settings$ofv_calc_type,
ds_index = poped_db$parameters$ds_index,
use_log = TRUE,
...
)
Arguments
ofv_init |
An initial objective function |
ofv_final |
A final objective function. |
poped_db |
a poped database |
npar |
The number of parameters to use for normalization. |
ofv_calc_type |
OFV calculation type for FIM
|
ds_index |
Ds_index is a vector set to 1 if a parameter is uninteresting, otherwise 0.
size=(1,num unfixed parameters). First unfixed bpop, then unfixed d, then unfixed docc and last unfixed sigma.
Default is the fixed effects being important, everything else not important. Used in conjunction with
|
use_log |
Are the 'ofv' arguments in the log space? |
... |
arguments passed to |
Value
The specified efficiency value depending on the ofv_calc_type.
The attribute "description" tells you how the calculation was made
attr(return_vale,"description")
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM).
Description
Compute the expectation of the FIM and OFV(FIM) given the model, parameters, distributions of parameter uncertainty, design and methods defined in the
PopED database. Some of the arguments coming from the PopED database can be overwritten;
by default these arguments are NULL in the
function, if they are supplied then they are used instead of the arguments from the PopED database.
Usage
evaluate.e.ofv.fim(
poped.db,
fim.calc.type = NULL,
bpop = poped.db$parameters$bpop,
d = poped.db$parameters$d,
covd = poped.db$parameters$covd,
docc = poped.db$parameters$docc,
sigma = poped.db$parameters$sigma,
model_switch = NULL,
ni = NULL,
xt = NULL,
x = NULL,
a = NULL,
groupsize = poped.db$design$groupsize,
deriv.type = NULL,
bLHS = poped.db$settings$bLHS,
ofv_calc_type = poped.db$settings$ofv_calc_type,
ED_samp_size = poped.db$settings$ED_samp_size,
use_laplace = poped.db$settings$iEDCalculationType,
laplace.fim = FALSE,
...
)
Arguments
poped.db |
A PopED database. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
covd |
Column major vector defining the covariances of the IIV variances.
That is, from your full IIV matrix |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
groupsize |
A vector of the number of individuals in each group. |
deriv.type |
A number indicating the type of derivative to use:
|
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
ofv_calc_type |
OFV calculation type for FIM
|
ED_samp_size |
Sample size for E-family sampling |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
laplace.fim |
Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done using the standard MC integration technique, so can be slow. |
... |
Other arguments passed to the function. |
Value
A list containing the E(FIM) and E(OFV(FIM)) and the a poped.db updated according to the function arguments.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Other E-family:
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot()
Other evaluate_FIM:
calc_ofv_and_fim(),
evaluate.fim(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samoples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## ED evaluate (with very few samples)
output <- evaluate.e.ofv.fim(poped.db,ED_samp_size=10)
output$E_ofv
## API evaluate (with very few samples)
output <- evaluate.e.ofv.fim(poped.db,ED_samp_size=10,ofv_calc_type=4)
output$E_ofv
## ED evaluate using Laplace approximation
tic()
output <- evaluate.e.ofv.fim(poped.db,use_laplace=TRUE)
toc()
output$E_ofv
## Not run:
## ED expected value with more precision.
## Compare time and value to Laplace approximation.
## Run a couple of times to see stochasticity of calculation.
tic()
e_ofv_mc <- evaluate.e.ofv.fim(poped.db,ED_samp_size=500)
toc()
e_ofv_mc$E_ofv
# If you want to get an E(FIM) from the laplace approximation you have to ask for it
# and it will take more time.
output <- evaluate.e.ofv.fim(poped.db,use_laplace=TRUE,laplace.fim=TRUE)
output$E_fim
## End(Not run)
Evaluate the Fisher Information Matrix (FIM)
Description
Compute the FIM given the model, parameters, design and methods defined in the
PopED database. Some of the arguments coming from the PopED database can be overwritten;
by default these arguments are NULL in the
function, if they are supplied then they are used instead of the arguments from the PopED database.
Usage
evaluate.fim(
poped.db,
fim.calc.type = NULL,
approx.method = NULL,
FOCE.num = NULL,
bpop.val = NULL,
d_full = NULL,
docc_full = NULL,
sigma_full = NULL,
model_switch = NULL,
ni = NULL,
xt = NULL,
x = NULL,
a = NULL,
groupsize = NULL,
deriv.type = NULL,
...
)
Arguments
poped.db |
A PopED database. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
approx.method |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI |
FOCE.num |
Number individuals in each step of FOCE approximation method |
bpop.val |
The fixed effects parameter values. Supplied as a vector. |
d_full |
A between subject variability matrix (OMEGA in NONMEM). |
docc_full |
A between occasion variability matrix. |
sigma_full |
A residual unexplained variability matrix (SIGMA in NONMEM). |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
groupsize |
A vector of the number of individuals in each group. |
deriv.type |
A number indicating the type of derivative to use:
|
... |
Other arguments passed to the function. |
Value
The FIM.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Other evaluate_design:
evaluate_design(),
evaluate_power(),
get_rse(),
model_prediction(),
plot_efficiency_of_windows(),
plot_model_prediction()
Other evaluate_FIM:
calc_ofv_and_fim(),
evaluate.e.ofv.fim(),
ofv_fim()
Examples
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun = ff.PK.1.comp.oral.sd.CL,
fg_fun = sfg,
fError_fun = feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
# notfixed_bpop=c(1,1,1,0),
notfixed_bpop=c(CL=1,V=1,KA=1,Favail=0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## evaluate initial design with the reduced FIM
FIM.1 <- evaluate.fim(poped.db)
FIM.1
det(FIM.1)
det(FIM.1)^(1/7)
get_rse(FIM.1,poped.db)
## evaluate initial design with the full FIM
FIM.0 <- evaluate.fim(poped.db,fim.calc.type=0)
FIM.0
det(FIM.0)
det(FIM.0)^(1/7)
get_rse(FIM.0,poped.db)
## evaluate initial design with the reduced FIM
## computing all derivatives with respect to the
## standard deviation of the residual unexplained variation
FIM.4 <- evaluate.fim(poped.db,fim.calc.type=4)
FIM.4
det(FIM.4)
get_rse(FIM.4,poped.db,fim.calc.type=4)
## evaluate initial design with the full FIM with A,B,C matricies
## should give same answer as fim.calc.type=0
FIM.5 <- evaluate.fim(poped.db,fim.calc.type=5)
FIM.5
det(FIM.5)
get_rse(FIM.5,poped.db,fim.calc.type=5)
## evaluate initial design with the reduced FIM with
## A,B,C matricies and derivative of variance
## should give same answer as fim.calc.type=1 (default)
FIM.7 <- evaluate.fim(poped.db,fim.calc.type=7)
FIM.7
det(FIM.7)
get_rse(FIM.7,poped.db,fim.calc.type=7)
## evaluate FIM and rse with prior FIM.1
poped.db.prior = create.poped.database(poped.db, prior_fim = FIM.1)
FIM.1.prior <- evaluate.fim(poped.db.prior)
all.equal(FIM.1.prior,FIM.1) # the FIM is only computed from the design in the poped.db
get_rse(FIM.1.prior,poped.db.prior) # the RSE is computed with the prior information
Evaluate a design
Description
This function evaluates the design defined in a poped database.
Usage
evaluate_design(poped.db, ...)
Arguments
poped.db |
A poped database |
... |
Extra parameters passed to |
Value
A list of elements evaluating the current design.
See Also
Other evaluate_design:
evaluate.fim(),
evaluate_power(),
get_rse(),
model_prediction(),
plot_efficiency_of_windows(),
plot_model_prediction()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
evaluate_design(poped.db)
Compute the Bayesian Fisher information matrix
Description
Computation of the Bayesian Fisher information matrix for individual parameters of a population model based on Maximum A Posteriori (MAP) estimation of the empirical Bayes estimates (EBEs) in a population model
Usage
evaluate_fim_map(
poped.db,
use_mc = FALSE,
num_sim_ids = 1000,
use_purrr = FALSE,
shrink_mat = F
)
Arguments
poped.db |
A PopED database |
use_mc |
Should the calculation be based on monte-carlo simulations. If not then then a first order approximation is used |
num_sim_ids |
If |
use_purrr |
If |
shrink_mat |
Should the shrinkage matrix be returned. Calculated as the inverse of the Bayesian Fisher information matrix times the inverse of the omega matrix (variance matrix of the between-subject variability). |
Value
The Bayesian Fisher information matrix for each design group
References
Combes, F. P., Retout, S., Frey, N., & Mentre, F. (2013). Prediction of shrinkage of individual parameters using the Bayesian information matrix in non-linear mixed effect models with evaluation in pharmacokinetics. Pharmaceutical Research, 30(9), 2355-67. doi:10.1007/s11095-013-1079-3.
Hennig, S., Nyberg, J., Fanta, S., Backman, J. T., Hoppu, K., Hooker, A. C., & Karlsson, M. O. (2012). Application of the optimal design approach to improve a pretransplant drug dose finding design for ciclosporin. Journal of Clinical Pharmacology, 52(3), 347-360. doi:10.1177/0091270010397731.
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
shrinkage(poped.db)
Power of a design to estimate a parameter.
Description
Evaluate the power of a design to estimate a parameter value different than some assumed value (often the assumed value is zero). The power is calculated using the linear Wald test and the the design is defined in a poped database.
Usage
evaluate_power(
poped.db,
bpop_idx,
h0 = 0,
alpha = 0.05,
power = 0.8,
twoSided = TRUE,
find_min_n = TRUE,
fim = NULL,
out = NULL,
...
)
Arguments
poped.db |
A poped database |
bpop_idx |
Index for an unfixed population parameter (bpop) for which the power should be evaluated for being different than the null hypothesis (h0). |
h0 |
The null hypothesized value for the parameter. |
alpha |
Type 1 error. |
power |
Targeted power. |
twoSided |
Is this a two-sided test. |
find_min_n |
Should the function compute the minimum n needed (given the current design) to achieve the desired power? |
fim |
Provide the FIM from a previous calculation |
out |
provide output from a previous calculation (e.g., calc_ofv_and_fim, ...) |
... |
Extra parameters passed to |
Value
A list of elements evaluating the current design including the power.
References
Retout, S., Comets, E., Samson, A., and Mentre, F. (2007). Design in nonlinear mixed effects models: Optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates. Statistics in Medicine, 26(28), 5162-5179. doi:10.1002/sim.2910.
Ueckert, S., Hennig, S., Nyberg, J., Karlsson, M. O., and Hooker, A. C. (2013). Optimizing disease progression study designs for drug effect discrimination. Journal of Pharmacokinetics and Pharmacodynamics, 40(5), 587-596. doi:10.1007/s10928-013-9331-3.
See Also
Other evaluate_design:
evaluate.fim(),
evaluate_design(),
get_rse(),
model_prediction(),
plot_efficiency_of_windows(),
plot_model_prediction()
Examples
# Folowing the examples presented in Retout, 2007
ff <- function(model_switch,xt,parameters,poped.db){
with(as.list(parameters),{
lambda1 <- lam1a
if(TREAT==2) lambda1 <- lam1b
y=log10(P1*exp(-lambda1*xt)+P2*exp(-lam2*xt))
return(list(y=y,poped.db=poped.db))
})
}
sfg <- function(x,a,bpop,b,bocc){
parameters=c(P1=exp(bpop[1]+b[1]),
P2=exp(bpop[2]+b[2]),
lam1a=exp(bpop[3]+b[3]),
lam1b=exp(bpop[3]+bpop[4]+b[3]),
lam2=exp(bpop[5]+b[4]),
TREAT=a[1])
return(parameters)
}
poped.db <- create.poped.database(ff_fun = ff,
fg_fun = sfg,
fError_fun = feps.add,
bpop=c(P1=12, P2=8,
lam1=-0.7,beta=0,lam2=-3.0),
d=c(P1=0.3, P2=0.3,
lam1=0.3,lam2=0.3),
sigma=c(0.065^2),
groupsize=100,
m=2,
xt=c(1, 3, 7, 14, 28, 56),
minxt=0,
maxxt=100,
a=list(c(TREAT=1),c(TREAT=2)))
plot_model_prediction(poped.db)
evaluate_design(poped.db)
poped.db_2 <- create.poped.database(poped.db,bpop=c(P1=12, P2=8,
lam1=-0.7,beta=0.262,lam2=-3.0))
plot_model_prediction(poped.db_2)
evaluate_design(poped.db_2)
evaluate_power(poped.db_2,bpop_idx = 4)
Extract a normalized group FIM
Description
Extract an individual FIM for each group in a design
Usage
extract_norm_group_fim(poped.db, ...)
Arguments
poped.db |
A PopED database |
... |
Arguments passed to |
Value
A list of FIMs, one for each group in a design.
RUV model: Additive .
Description
This is a residual unexplained variability (RUV) model function that encodes the model described above.
The function is suitable for input to the create.poped.database function using the
fError_file argument.
Usage
feps.add(model_switch, xt, parameters, epsi, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
epsi |
A matrix with the same number of rows as the |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other RUV_models:
feps.add.prop(),
feps.prop()
Examples
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.KE
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(KE=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.KE,
fg_fun=sfg,
fError_fun=feps.add,
bpop=c(KE=0.15/8, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(KE=0.07, V=0.02, KA=0.6),
sigma=1,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
RUV model: Additive and Proportional.
Description
This is a residual unexplained variability (RUV) model function that encodes the model described above.
The function is suitable for input to the create.poped.database function using the
fError_file argument.
Usage
feps.add.prop(model_switch, xt, parameters, epsi, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
epsi |
A matrix with the same number of rows as the |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other RUV_models:
feps.add(),
feps.prop()
Examples
library(PopED)
## find the parameters that are needed to define in the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
return( parameters )
}
## -- Define design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.md.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
groupsize=20,
m=2,
sigma=c(0.04,5e-6),
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9),
d=c(V=0.09,KA=0.09,CL=0.25^2),
notfixed_bpop=c(1,1,1,0),
notfixed_sigma=c(0,0),
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
a=cbind(c(20,40),c(24,24)),
bUseGrouped_xt=1,
maxa=c(200,24),
mina=c(0,24))
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
RUV model: Proportional.
Description
This is a residual unexplained variability (RUV) model function that encodes the model described above.
The function is suitable for input to the create.poped.database function using the
fError_file argument.
Usage
feps.prop(model_switch, xt, parameters, epsi, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
epsi |
A matrix with the same number of rows as the |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other RUV_models:
feps.add(),
feps.add.prop()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
MATLAB feval function
Description
This is just a wrapper for the do.call function to behave like the feval function in MATLAB.
Usage
feval(file.name, ...)
Arguments
file.name |
A function or a string that is the name of a function. |
... |
Arguments for the function. Multiple arguments separated by a comma. |
Value
Output from the defined function.
See Also
Other MATLAB:
cell(),
diag_matlab(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
feval("sin",pi/2)
Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using CL.
Description
This is a structural model function that encodes a model that is
one-compartment, oral absorption, multiple bolus dose, parameterized using CL.
The function is suitable for input to the create.poped.database function using the
ff_fun or ff_file argument.
Usage
ff.PK.1.comp.oral.md.CL(model_switch, xt, parameters, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other structural_models:
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Examples
library(PopED)
## find the parameters that are needed to define in the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
return( parameters )
}
## -- Define design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.md.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
groupsize=20,
m=2,
sigma=c(0.04,5e-6),
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9),
d=c(V=0.09,KA=0.09,CL=0.25^2),
notfixed_bpop=c(1,1,1,0),
notfixed_sigma=c(0,0),
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
a=cbind(c(20,40),c(24,24)),
bUseGrouped_xt=1,
maxa=c(200,24),
mina=c(0,24))
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using KE.
Description
This is a structural model function that encodes a model that is
one-compartment, oral absorption, multiple bolus dose, parameterized using KE.
The function is suitable for input to the create.poped.database function using the
ff_fun or ff_file argument.
Usage
ff.PK.1.comp.oral.md.KE(model_switch, xt, parameters, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other structural_models:
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Examples
library(PopED)
## find the parameters that are needed to define in the structural model
ff.PK.1.comp.oral.md.KE
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
## -- parameter definition function
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
KE=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
return( parameters )
}
## -- Define design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.md.KE,
fg_fun=sfg,
fError_fun=feps.add.prop,
groupsize=20,
m=2,
sigma=c(0.04,5e-6),
bpop=c(V=72.8,KA=0.25,KE=3.75/72.8,Favail=0.9),
d=c(V=0.09,KA=0.09,KE=0.25^2),
notfixed_bpop=c(1,1,1,0),
notfixed_sigma=c(0,0),
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
a=cbind(c(20,40),c(24,24)),
bUseGrouped_xt=1,
maxa=c(200,40),
mina=c(0,2))
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
Structural model: one-compartment, oral absorption, single bolus dose, parameterized using CL.
Description
This is a structural model function that encodes a model that is
one-compartment, oral absorption, single bolus dose, parameterized using CL.
The function is suitable for input to the create.poped.database function using the
ff_fun or ff_file argument.
Usage
ff.PK.1.comp.oral.sd.CL(model_switch, xt, parameters, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other structural_models:
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
Structural model: one-compartment, oral absorption, single bolus dose, parameterized using KE.
Description
This is a structural model function that encodes a model that is
one-compartment, oral absorption, single bolus dose, parameterized using KE.
The function is suitable for input to the create.poped.database function using the
ff_fun or ff_file argument.
Usage
ff.PK.1.comp.oral.sd.KE(model_switch, xt, parameters, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other structural_models:
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Examples
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.KE
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(KE=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.KE,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(KE=0.15/8, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(KE=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
Structural model: one-compartment, oral absorption, multiple bolus dose, parameterized using CL driving an inhibitory IMAX model with a direct effect.
Description
This is a structural model function that encodes the model described above.
The function is suitable for input to the create.poped.database function using the
ff_fun or ff_file argument.
Usage
ff.PKPD.1.comp.oral.md.CL.imax(model_switch, xt, parameters, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.sd.CL.emax()
Other structural_models:
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.sd.CL.emax()
Examples
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PKPD.1.comp.oral.md.CL.imax
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
## -- parameter definition function
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU = a[2],
E0=bpop[5]*exp(b[4]),
IMAX=bpop[6],
IC50=bpop[7])
return( parameters )
}
feps <- function(model_switch,xt,parameters,epsi,poped.db){
## -- Residual Error function
returnArgs <- do.call(poped.db$model$ff_pointer,list(model_switch,xt,parameters,poped.db))
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
MS <- model_switch
pk.dv <- y*(1+epsi[,1])+epsi[,2]
pd.dv <- y*(1+epsi[,3])+epsi[,4]
y[MS==1] = pk.dv[MS==1]
y[MS==2] = pd.dv[MS==2]
return(list( y= y,poped.db =poped.db ))
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PKPD.1.comp.oral.md.CL.imax,
fError_fun=feps,
fg_fun=sfg,
groupsize=20,
m=3,
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9,
E0=1120,IMAX=0.807,IC50=0.0993),
notfixed_bpop=c(1,1,1,0,1,1,1),
d=c(V=0.09,KA=0.09,CL=0.25^2,E0=0.09),
sigma=c(0.04,5e-6,0.09,100),
notfixed_sigma=c(0,0,0,0),
xt=c( 1,2,8,240,240,1,2,8,240,240),
minxt=c(0,0,0,240,240,0,0,0,240,240),
maxxt=c(10,10,10,248,248,10,10,10,248,248),
G_xt=c(1,2,3,4,5,1,2,3,4,5),
model_switch=c(1,1,1,1,1,2,2,2,2,2),
a=cbind(c(20,40,0),c(24,24,24)),
bUseGrouped_xt=1,
ourzero=0,
maxa=c(200,40),
mina=c(0,2))
## create plot of model without variability
plot_model_prediction(poped.db,facet_scales="free")
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
Structural model: one-compartment, single bolus IV dose, parameterized using CL driving an EMAX model with a direct effect.
Description
This is a structural model function that encodes the model described above.
The function is suitable for input to the create.poped.database function using the
ff_fun or ff_file argument.
Usage
ff.PKPD.1.comp.sd.CL.emax(model_switch, xt, parameters, poped.db)
Arguments
model_switch |
a vector of values, the same size as |
xt |
a vector of independent variable values (often time). |
parameters |
A named list of parameter values. |
poped.db |
a poped database. This can be used to extract information that may be needed in the model file. |
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See Also
Other models:
feps.add(),
feps.add.prop(),
feps.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax()
Other structural_models:
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax()
Examples
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PKPD.1.comp.sd.CL.emax
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
## -- parameter definition function
parameters=c(
CL=bpop[1]*exp(b[1]) ,
V=bpop[2]*exp(b[2]) ,
E0=bpop[3]*exp(b[3]) ,
EMAX=bpop[4]*exp(b[4]) ,
EC50=bpop[5]*exp(b[5]) ,
DOSE=a[1]
)
return( parameters )
}
feps <- function(model_switch,xt,parameters,epsi,poped.db){
## -- Residual Error function
## -- Proportional PK + additive PD
returnArgs <- do.call(poped.db$model$ff_pointer,list(model_switch,xt,parameters,poped.db))
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
MS <- model_switch
prop.err <- y*(1+epsi[,1])
add.err <- y+epsi[,2]
y[MS==1] = prop.err[MS==1]
y[MS==2] = add.err[MS==2]
return(list( y= y,poped.db =poped.db ))
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PKPD.1.comp.sd.CL.emax,
fError_fun=feps,
fg_fun=sfg,
groupsize=20,
m=3,
sigma=diag(c(0.15,0.15)),
bpop=c(CL=0.5,V=0.2,E0=1,EMAX=1,EC50=1),
d=c(CL=0.01,V=0.01,E0=0.01,EMAX=0.01,EC50=0.01),
xt=c( 0.33,0.66,0.9,5,0.1,1,2,5),
model_switch=c( 1,1,1,1,2,2,2,2),
minxt=0,
maxxt=5,
a=rbind(2.75,5,10),
bUseGrouped_xt=1,
maxa=10,
mina=0.1)
## create plot of model without variability
plot_model_prediction(poped.db,facet_scales="free")
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
det(FIM)
get_rse(FIM,poped.db)
MATLAB fileparts function
Description
Get the various parts of a file with path string.
Usage
fileparts(filename.with.path)
Arguments
filename.with.path |
A string of a filename with a path |
Value
A list with the following components:
pathname |
The path name |
filename |
The file name |
fileext |
The file extension |
Note
This is a modified version of the same function in the matlab R-package.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
isempty(),
ones(),
rand(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
fileparts("ggg/ttt/lll.R")
Generate a random sample from a truncated normal distribution.
Description
Generate a random sample from a truncated normal distribution.
Usage
getTruncatedNormal(mean, variance)
Arguments
mean |
the mean of the normal distribution |
variance |
The variance of the normal distribution |
Value
A random sample from the specified truncated normal distribution
Examples
getTruncatedNormal(mean=3,variance=100)
Extract all model parameters from the PopED database.
Description
Extract all model parameters from the PopED database.
Usage
get_all_params(poped.db)
Arguments
poped.db |
A PopED database. |
Value
A list containing:
bpop |
A vector of fixed effect parameter values. |
d |
A vector of between subject variability parameters |
covd |
A vector of the covariances of the between subject variability parameters. Row major format of the lower triangular portion of the D (OMEGA) matrix |
docc |
A vector of the between occasion variability (BOV) terms in the model |
covdocc |
A vector of the covariances between the BOV terms. Row major of the lower triangular portion of the BOV matrix. |
sigma |
A vector of the residual unexplained variances (RUV) |
covsigma |
A vector of the covariances between the RUV terms |
all |
A vector with all of the above, in the order of this list. |
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
get_all_params(poped.db)
Compute the expected parameter relative standard errors
Description
This function computes the expected relative standard errors of a model given a design and a previously computed FIM.
Usage
get_rse(
fim,
poped.db,
bpop = poped.db$parameters$bpop[, 2],
d = poped.db$parameters$d[, 2],
docc = poped.db$parameters$docc,
sigma = poped.db$parameters$sigma,
use_percent = TRUE,
fim.calc.type = poped.db$settings$iFIMCalculationType,
prior_fim = poped.db$settings$prior_fim,
...
)
Arguments
fim |
A Fisher Information Matrix (FIM). |
poped.db |
A PopED database. |
bpop |
A vector containing the values of the fixed effects used to compute the |
d |
A vector containing the values of the diagonals of the between subject variability matrix. |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
use_percent |
Should RSE be reported as percent? |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
prior_fim |
A prior FIM to be added to the |
... |
Additional arguments passed to |
Value
A named list of RSE values. If the estimated parameter is assumed to be zero then for that parameter the standard error is returned.
See Also
Other evaluate_design:
evaluate.fim(),
evaluate_design(),
evaluate_power(),
model_prediction(),
plot_efficiency_of_windows(),
plot_model_prediction()
Examples
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun = ff.PK.1.comp.oral.sd.CL,
fg_fun = sfg,
fError_fun = feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
# notfixed_bpop=c(1,1,1,0),
notfixed_bpop=c(CL=1,V=1,KA=1,Favail=0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## evaluate initial design with the reduced FIM
FIM.1 <- evaluate.fim(poped.db)
FIM.1
det(FIM.1)
det(FIM.1)^(1/7)
get_rse(FIM.1,poped.db)
## evaluate initial design with the full FIM
FIM.0 <- evaluate.fim(poped.db,fim.calc.type=0)
FIM.0
det(FIM.0)
det(FIM.0)^(1/7)
get_rse(FIM.0,poped.db)
## evaluate initial design with the reduced FIM
## computing all derivatives with respect to the
## standard deviation of the residual unexplained variation
FIM.4 <- evaluate.fim(poped.db,fim.calc.type=4)
FIM.4
det(FIM.4)
get_rse(FIM.4,poped.db,fim.calc.type=4)
## evaluate initial design with the full FIM with A,B,C matricies
## should give same answer as fim.calc.type=0
FIM.5 <- evaluate.fim(poped.db,fim.calc.type=5)
FIM.5
det(FIM.5)
get_rse(FIM.5,poped.db,fim.calc.type=5)
## evaluate initial design with the reduced FIM with
## A,B,C matricies and derivative of variance
## should give same answer as fim.calc.type=1 (default)
FIM.7 <- evaluate.fim(poped.db,fim.calc.type=7)
FIM.7
det(FIM.7)
get_rse(FIM.7,poped.db,fim.calc.type=7)
## evaluate FIM and rse with prior FIM.1
poped.db.prior = create.poped.database(poped.db, prior_fim = FIM.1)
FIM.1.prior <- evaluate.fim(poped.db.prior)
all.equal(FIM.1.prior,FIM.1) # the FIM is only computed from the design in the poped.db
get_rse(FIM.1.prior,poped.db.prior) # the RSE is computed with the prior information
Return all the unfixed parameters
Description
all = vector of all unfixed params var derivative is a vector of 1 and 0, 1 means derivative of parameter is taken w.r.t. variance otherwise w.r.t. sd If params is supplied then the parameter is taken from this vector instead of poped.db
Usage
get_unfixed_params(poped.db, params = NULL)
Arguments
poped.db |
a PopED database. |
params |
If params is supplied then the parameters are taken from this vector. |
Value
A list with the parameters. All unfixed parameters are also
returned in the "all output with the specified order
(bpop,d,covd,docc,covdocc,sigma,covsigma). var_derivative is a
vector of 1's or 0's, 1 means derivative of parameter is taken with respect
to the variance otherwise with respect to standard deviation.
Create a full D (between subject variability) matrix given a vector of variances and covariances. Note, this does not test matching vector lengths.
Description
Create a full D (between subject variability) matrix given a vector of variances and covariances. Note, this does not test matching vector lengths.
Usage
getfulld(variance_vector, covariance_vector = NULL)
Arguments
variance_vector |
The vector of the variances. |
covariance_vector |
A vector of the covariances. Written in column major order for the lower triangular matrix. |
Value
The full matrix of variances for the between subject variances
Examples
getfulld(c(1,2,3))
getfulld(c(1,2,3),c(7,6,5))
Model linearization with respect to epsilon.
Description
The function performs a linearization of the model with respect to the residual variability. Derivative of model w.r.t. eps evaluated at eps=0 and b=b_ind.
Usage
gradf_eps(model_switch, xt_ind, x, a, bpop, b_ind, bocc_ind, num_eps, poped.db)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
xt_ind |
A vector of the individual/group sample times |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
b_ind |
vector of individual realization of the BSV terms b |
bocc_ind |
Vector of individual realizations of the BOV terms bocc |
num_eps |
The number of |
poped.db |
A PopED database. |
Value
A matrix of size (samples per individual x number of epsilons)
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
#for the FO approximation
ind=1
gradf_eps(model_switch=t(poped.db$design$model_switch[ind,,drop=FALSE]),
xt_ind=t(poped.db$design$xt[ind,,drop=FALSE]),
x=zeros(0,1),
a=t(poped.db$design$a[ind,,drop=FALSE]),
bpop=poped.db$parameters$bpop[,2,drop=FALSE],
b_ind=zeros(poped.db$parameters$NumRanEff,1),
bocc_ind=zeros(poped.db$parameters$NumDocc,1),
num_eps=size(poped.db$parameters$sigma,1),
poped.db)["dfeps_de0"]
Compute the inverse of a matrix
Description
Function computes the inverse of a matrix.
Usage
inv(mat, method = 1, tol = .Machine$double.eps, pseudo_on_fail = TRUE, ...)
Arguments
mat |
A matrix |
method |
Which method to use. 1 is Cholesky |
tol |
The tolerance at which we should identify a singular value as zero (used in pseudoinverse calculation). |
pseudo_on_fail |
If another method fails should the Moore-Penrose generalized inverse (pseudoinverse) be used? |
... |
Not used. |
Value
The inverse matrix
Function written to match MATLAB's isempty function
Description
Function written to match MATLAB's isempty function
Usage
isempty(...)
Arguments
... |
arguments to pass to the function. Typically a matrix. |
Value
Logical. True if the passed object has any dimension that is zero.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
ones(),
rand(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
isempty(zeros(2,3))
isempty(zeros(2,0))
isempty(c(1,2,3))
Compute the natural log of the PDF for the parameters in an E-family design
Description
Compute the natural log of the PDF for the parameters in an E-family design
Usage
log_prior_pdf(
alpha,
bpopdescr,
ddescr,
return_gradient = F,
return_hessian = F
)
Arguments
alpha |
A parameter vector. |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
return_gradient |
Should the gradient be returned. |
return_hessian |
Should the hessian be returned? |
Compute the monte-carlo mean of a function
Description
Function computes the monte-carlo mean of a function by varying the parameter inputs to the function
Usage
mc_mean(
ofv_fcn,
poped.db,
bpopdescr = poped.db$parameters$bpop,
ddescr = poped.db$parameters$d,
doccdescr = poped.db$parameters$d,
user_distribution_pointer = poped.db$model$user_distribution_pointer,
ED_samp_size = poped.db$settings$ED_samp_size,
bLHS = poped.db$settings$bLHS,
...
)
Arguments
ofv_fcn |
A function with poped.db as the first input |
poped.db |
A PopED database. |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
doccdescr |
Matrix defining the IOV. per row (row number = parameter_number) we should have:
|
user_distribution_pointer |
Function name for user defined distributions for E-family designs |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
... |
Other arguments passed to the function. |
Value
The mean of the function evaluated at different parameter values.
Wrap summary functions from Hmisc and ggplot to work with stat_summary in ggplot
Description
Created for back compatibility with older versions of ggplot, and so that PopED does not have to load ggplot when started.
Usage
median_hilow_poped(x, ...)
Arguments
x |
A numeric vector |
... |
Additional arguments passed to Hmisc's smedian.hilow function or ggplot2's median_hilow function, depending on your version of ggplot. |
The Fisher Information Matrix (FIM) for one individual
Description
Compute the FIM for one individual given specific model(s), parameters, design and methods.
Usage
mf3(model_switch, xt, x, a, bpop, d, sigma, docc, poped.db)
Arguments
model_switch |
A vector that is the same size as xt, specifying which model each sample belongs to. |
xt |
A vector of sample times. |
x |
A vector for the discrete design variables. |
a |
A vector of covariates. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
d |
A between subject variability matrix (OMEGA in NONMEM). |
sigma |
A residual unexplained variability matrix (SIGMA in NONMEM). |
docc |
A between occasion variability matrix. |
poped.db |
A PopED database. |
Value
As a list:
ret |
The FIM for one individual |
poped.db |
A PopED database |
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
The full Fisher Information Matrix (FIM) for one individual Calculating one model switch at a time, good for large matrices.
Description
Compute the full FIM for one individual given specific model(s), parameters, design and methods. This computation calculates the FIM for each model switch separately. Correlations between the models parameters are assumed to be zero.
Usage
mf7(model_switch, xt_ind, x, a, bpop, d, sigma, docc, poped.db)
Arguments
model_switch |
A vector that is the same size as xt, specifying which model each sample belongs to. |
x |
A vector for the discrete design variables. |
a |
A vector of covariates. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
d |
A between subject variability matrix (OMEGA in NONMEM). |
sigma |
A residual unexplained variability matrix (SIGMA in NONMEM). |
docc |
A between occasion variability matrix. |
poped.db |
A PopED database. |
Value
As a list:
ret |
The FIM for one individual |
poped.db |
A PopED database |
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mftot(),
ofv_criterion(),
ofv_fim()
Modified Fedorov Exchange Algorithm
Description
Optimize the objective function using a modified Fedorov exchange algorithm. The function works for continuous and discrete optimization variables. This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
Usage
mfea(
poped.db,
model_switch,
ni,
xt,
x,
a,
bpopdescr,
ddescr,
maxxt,
minxt,
maxa,
mina,
fmf,
dmf,
EAStepSize = poped.db$settings$EAStepSize,
ourzero = poped.db$settings$ourzero,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
trflag = T,
...
)
Arguments
poped.db |
A PopED database. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
maxa |
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value |
mina |
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
EAStepSize |
Exchange Algorithm StepSize |
ourzero |
Value to interpret as zero in design |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
trflag |
Should the optimization be output to the screen and to a file? |
... |
arguments passed to |
References
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# typically one will use poped_optimize
# This then calls mfea
##############
# optimization of covariate, with coarse grid
out_1 <- poped_optimize(poped.db,opt_a=1,
bUseExchangeAlgorithm=1,
EAStepSize=25,out_file = "")
## Not run:
# MFEA optimization with only integer times allowed
out_2 <- poped_optimize(poped.db,opt_xt=1,
bUseExchangeAlgorithm=1,
EAStepSize=1)
get_rse(out_2$fmf,out_2$poped.db)
plot_model_prediction(out_2$poped.db)
##############
# If you really want to you can use mfea dirtectly
##############
dsl <- downsizing_general_design(poped.db)
output <- mfea(poped.db,
model_switch=dsl$model_switch,
ni=dsl$ni,
xt=dsl$xt,
x=dsl$x,
a=dsl$a,
bpopdescr=dsl$bpop,
ddescr=dsl$d,
maxxt=dsl$maxxt,
minxt=dsl$minxt,
maxa=dsl$maxa,
mina=dsl$mina,
fmf=0,dmf=0,
EAStepSize=1,
opt_xt=1)
## End(Not run)
Evaluate the Fisher Information Matrix (FIM)
Description
Compute the FIM given specific model(s), parameters, design and methods.
Usage
mftot(
model_switch,
groupsize,
ni,
xt,
x,
a,
bpop,
d,
sigma,
docc,
poped.db,
...
)
Arguments
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
groupsize |
A vector of the number of individuals in each group. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
d |
A between subject variability matrix (OMEGA in NONMEM). |
sigma |
A residual unexplained variability matrix (SIGMA in NONMEM). |
docc |
A between occasion variability matrix. |
poped.db |
A PopED database. |
Value
As a list:
ret |
The FIM |
poped.db |
A PopED database |
See Also
For an easier function to use, please see evaluate.fim.
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
ofv_criterion(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
mftot(model_switch=poped.db$design$model_switch,
groupsize=poped.db$design$groupsize,
ni=poped.db$design$ni,
xt=poped.db$design$xt,
x=poped.db$design$x,
a=poped.db$design$a,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
sigma=poped.db$parameters$sigma,
docc=poped.db$parameters$param.pt.val$docc,
poped.db)["ret"]
Model predictions
Description
Function generates a data frame of model predictions for the typical value in the population, individual predictions and data predictions. The function can also be used to generate datasets without predictions using the design specified in the arguments.
Usage
model_prediction(
poped.db = NULL,
design = list(xt = poped.db$design[["xt"]], groupsize = poped.db$design$groupsize, m =
poped.db$design[["m"]], x = poped.db$design[["x"]], a = poped.db$design[["a"]], ni =
poped.db$design$ni, model_switch = poped.db$design$model_switch),
model = list(fg_pointer = poped.db$model$fg_pointer, ff_pointer =
poped.db$model$ff_pointer, ferror_pointer = poped.db$model$ferror_pointer),
parameters = list(docc = poped.db$parameters$docc, d = poped.db$parameters$d, bpop =
poped.db$parameters$bpop, covd = poped.db$parameters$covd, covdocc =
poped.db$parameters$covdocc, sigma = poped.db$parameters$sigma),
IPRED = FALSE,
DV = FALSE,
dosing = NULL,
predictions = NULL,
filename = NULL,
models_to_use = "all",
model_num_points = NULL,
model_minxt = NULL,
model_maxxt = NULL,
include_sample_times = T,
groups_to_use = "all",
include_a = TRUE,
include_x = TRUE,
manipulation = NULL,
PI = FALSE,
PI_conf_level = 0.95,
PI_ln_dist = TRUE
)
Arguments
poped.db |
A PopED database created by |
design |
A list that is passed as arguments to the function |
model |
A list containing the model elements to use for the predictions |
parameters |
A list of parameters to use in the model predictions. |
IPRED |
Should we simulate individual predictions? |
DV |
should we simulate observations? |
dosing |
A list of lists that adds dosing records to the data frame (Each inner list corresponding to a group in the design). |
predictions |
Should the resulting data frame have predictions? Either |
filename |
A filename that the data frame should be written to in comma separate value (csv) format. |
models_to_use |
Which model numbers should we use?
Model numbers are defined in |
model_num_points |
How many extra observation rows should be created in the data frame for each group or individual
per model. If used then the points are placed evenly between |
model_minxt |
The minimum time value for extra observation rows indicated by |
model_maxxt |
The minimum time value for extra observation rows indicated by |
include_sample_times |
Should observations rows in the output data frame include the times indicated in the input design? |
groups_to_use |
Which groups should we include in the output data frame?Allowed values are |
include_a |
Should we include the continuous design variables in the output? |
include_x |
Should we include the discrete design variables in the output? |
manipulation |
A list of one or more |
PI |
Compute prediction intervals for the data given the model. Predictions are based on first-order approximations to the model variance and a log-normality assumption of that variance (by default), if all predictions are positive, otherwise a normal distribution is assumed. |
PI_conf_level |
The confidence level for the prediction interval computed. |
PI_ln_dist |
Should the PI calculation be done assuming log-normal or a normal distribution. TRUE is the default and indicates a log-normal distribution. If any of the PRED values from the model are negative then a normal distribution is assumed. |
Value
A dataframe containing a design and (potentially) simulated data with some dense grid of samples and/or based on the input design.
See Also
Other evaluate_design:
evaluate.fim(),
evaluate_design(),
evaluate_power(),
get_rse(),
plot_efficiency_of_windows(),
plot_model_prediction()
Other Simulation:
plot_efficiency_of_windows(),
plot_model_prediction()
Examples
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## data frame with model predictions
df_1 <- model_prediction(poped.db)
head(df_1,n=20)
## data frame with variability
df_2 <- model_prediction(poped.db,DV=TRUE)
head(df_2,n=20)
## data frame with variability (only IPRED, no DV)
df_3 <- model_prediction(poped.db,IPRED=TRUE)
head(df_3,n=20)
## data frame with model predictions, no continuous design variables in data frame
df_4 <- model_prediction(poped.db,include_a = FALSE)
head(df_4,n=20)
## -- 2 groups
poped.db.2 <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=rbind(3,3),
m=2,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=rbind(70,50))
df_5 <- model_prediction(poped.db.2,DV=TRUE)
head(df_5,n=20)
## without a poped database, just describing the design
## Useful for creating datasets for use in other software (like NONMEM)
design_1 <- list(
xt=c( 0.5,1,2,6,24,36,72,120),
m=2,
groupsize=3)
design_2 <- list(
xt=c( 0.5,1,2,6,24,36,72,120),
m=2,
groupsize=3,
a=c(WT=70,AGE=50))
design_3 <- list(
xt=c( 0.5,1,2,6,24,36,72,120),
m=2,
groupsize=3,
a=list(c(WT=70,AGE=50),c(AGE=45,WT=60)))
(df_6 <- model_prediction(design=design_1))
(df_7 <- model_prediction(design=design_2))
(df_8 <- model_prediction(design=design_3))
(df_9 <- model_prediction(design=design_3,DV=TRUE))
# generate random deviations in WT for each individual
df_10 <- model_prediction(design=design_3,DV=TRUE,
manipulation=expression({for(id in unique(ID))
WT[ID==id] = rnorm(1,WT[ID==id],WT[ID==id]*0.1);id <- NULL}))
head(df_10,n=20)
# generate random deviations in WT and AGE for each individual
df_11 <- model_prediction(design=design_3,DV=TRUE,
manipulation=list(
expression(for(id in unique(ID))
WT[ID==id] = rnorm(1,WT[ID==id],WT[ID==id]*0.1)),
expression(for(id in unique(ID))
AGE[ID==id] = rnorm(1,AGE[ID==id],AGE[ID==id]*0.2)),
expression(id <- NULL)
))
head(df_10,n=20)
## create dosing rows
dosing_1 <- list(list(AMT=1000,RATE=NA,Time=0.5),list(AMT=3000,RATE=NA,Time=0.5))
dosing_2 <- list(list(AMT=1000,RATE=NA,Time=0.5))
dosing_3 <- list(list(AMT=1000,Time=0.5))
dosing_4 <- list(list(AMT=c(1000,20),Time=c(0.5,10))) # multiple dosing
(df_12 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_1))
(df_13 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_2))
(df_14 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_3))
(df_15 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_4))
model_prediction(design=design_3,DV=TRUE,dosing=dosing_4,model_num_points = 10)
model_prediction(design=design_3,DV=TRUE,dosing=dosing_4,model_num_points = 10,model_minxt=20)
design_4 <- list(
xt=c( 0.5,1,2,6,24,36,72,120),
model_switch=c(1,1,1,1,2,2,2,2),
m=2,
groupsize=3,
a=list(c(WT=70,AGE=50),c(AGE=45,WT=60)))
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4)
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = 10)
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = 10,
model_minxt=10,model_maxxt=100)
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = 10,
model_minxt=c(20,20),model_maxxt=c(100,100))
model_prediction(design=design_4,DV=TRUE,dosing=dosing_4,model_num_points = c(10,10),
model_minxt=c(20,20),model_maxxt=c(100,100))
Normalize an objective function by the size of the FIM matrix
Description
Compute a normalized OFV based on the size of the FIM matrix. This value can then be used in
efficiency calculations. This is NOT the OFV used in optimization, see ofv_fim.
Usage
ofv_criterion(
ofv_f,
num_parameters,
poped.db,
ofv_calc_type = poped.db$settings$ofv_calc_type
)
Arguments
ofv_f |
An objective function |
num_parameters |
The number of parameters to use for normalization |
poped.db |
a poped database |
ofv_calc_type |
OFV calculation type for FIM
|
Value
The specified criterion value.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
## evaluate initial design
FIM <- evaluate.fim(poped.db) # new name for function needed
FIM
get_rse(FIM,poped.db)
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=1),
length(get_unfixed_params(poped.db)[["all"]]),
poped.db,
ofv_calc_type=1) # det(FIM)
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=2),
length(get_unfixed_params(poped.db)[["all"]]),
poped.db,
ofv_calc_type=2)
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=4),
length(get_unfixed_params(poped.db)[["all"]]),
poped.db,
ofv_calc_type=4)
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=6),
length(get_unfixed_params(poped.db)[["all"]]),
poped.db,
ofv_calc_type=6)
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=7),
length(get_unfixed_params(poped.db)[["all"]]),
poped.db,
ofv_calc_type=7)
Evaluate a criterion of the Fisher Information Matrix (FIM)
Description
Compute a criterion of the FIM given the model, parameters, design and methods defined in the PopED database.
Usage
ofv_fim(
fmf,
poped.db,
ofv_calc_type = poped.db$settings$ofv_calc_type,
ds_index = poped.db$parameters$ds_index,
use_log = TRUE,
...
)
Arguments
fmf |
The FIM |
poped.db |
A poped database |
ofv_calc_type |
OFV calculation type for FIM
|
ds_index |
Ds_index is a vector set to 1 if a parameter is uninteresting, otherwise 0.
size=(1,num unfixed parameters). First unfixed bpop, then unfixed d, then unfixed docc and last unfixed sigma.
Default is the fixed effects being important, everything else not important. Used in conjunction with
|
use_log |
Should the criterion be in the log domain? |
... |
arguments passed to |
Value
The specified criterion value.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion()
Other evaluate_FIM:
calc_ofv_and_fim(),
evaluate.e.ofv.fim(),
evaluate.fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
get_rse(FIM,poped.db)
det(FIM)
ofv_fim(FIM,poped.db,ofv_calc_type=1) # det(FIM)
ofv_fim(FIM,poped.db,ofv_calc_type=2) # 1/trace_matrix(inv(FIM))
ofv_fim(FIM,poped.db,ofv_calc_type=4) # log(det(FIM))
ofv_fim(FIM,poped.db,ofv_calc_type=6) # Ds with fixed effects as "important"
ofv_fim(FIM,poped.db,ofv_calc_type=6,
ds_index=c(1,1,1,0,0,0,1,1)) # Ds with random effects as "important"
ofv_fim(FIM,poped.db,ofv_calc_type=7) # 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))
Create a matrix of ones
Description
Create a matrix of ones of size (dim1 x dim2).
Usage
ones(dim1, dim2 = NULL)
Arguments
dim1 |
The dimension of the matrix (if square) or the number of rows. |
dim2 |
The number of columns |
Value
A matrix of ones
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
rand(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
ones(4)
ones(3,4)
Optimize a function using adaptive random search.
Description
Optimize an objective function using an adaptive random search algorithm. The function works for both discrete and continuous optimization parameters and allows for box-constraints and sets of allowed values.
Usage
optim_ARS(
par,
fn,
lower = NULL,
upper = NULL,
allowed_values = NULL,
loc_fac = 4,
no_bounds_sd = par,
iter = 400,
iter_adapt = 50,
adapt_scale = 1,
max_run = 200,
trace = TRUE,
trace_iter = 5,
new_par_max_it = 200,
maximize = F,
parallel = F,
parallel_type = NULL,
num_cores = NULL,
mrgsolve_model = NULL,
seed = round(runif(1, 0, 1e+07)),
allow_replicates = TRUE,
replicates_index = seq(1, length(par)),
generator = NULL,
...
)
Arguments
par |
A vector of initial values for the parameters to be optimized over. |
fn |
A function to be minimized (or maximized), with first argument the vector of parameters over which minimization is to take place. It should return a scalar result. |
lower |
Lower bounds on the parameters. A vector. |
upper |
Upper bounds on the parameters. A vector. |
allowed_values |
A list containing allowed values for each parameter |
loc_fac |
Locality factor for determining the standard deviation of the sampling distribution around the current
position of the parameters. The initial standard deviation is normally calculated as |
no_bounds_sd |
The standard deviation of the sampling distribution around the current
position of the parameters when there are no upper or lower limits (e.g. when |
iter |
The number of iterations for the algorithm to perform (this is a maximum number, it could be less). |
iter_adapt |
The number of iterations before adapting (shrinking) the parameter search space. |
adapt_scale |
The scale for adapting the size of the sampling distribution. The adaptation of the
standard deviation of the sampling distribution around the current
position of the parameters is done after |
max_run |
The maximum number of iterations to run without a change in the best parameter estimates. |
trace |
Should the algorithm output results intermittently. |
trace_iter |
How many iterations between each update to the screen about the result of the search. |
new_par_max_it |
The algorithm randomly chooses samples based on the current best set of parameters. If when drawing
these samples the new parameter set has already been tested then a new draw is performed. After |
maximize |
Should the function be maximized? Default is to minimize. |
parallel |
Should we use parallel computations? |
parallel_type |
Which type of parallelization should be used?
Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on
Linux-like systems. By default this is chosen for you depending on your operating system.
See |
num_cores |
The number of cores to use in the parallelization. By default is set to the number
output from
|
mrgsolve_model |
If the computations require a mrgsolve model and you
are using the "snow" method then you need to specify the name of the model
object created by |
seed |
The random seed to use in the algorithm, |
allow_replicates |
Should the algorithm allow parameters to have the same value? |
replicates_index |
A vector, the same length as the parameters. If two values are the same in this vector then the parameters may not assume the same value in the optimization. |
generator |
A user-defined function that generates new parameter sets to try in the algorithm. See examples below. |
... |
Additional arguments passed to |
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
## "wild" function , global minimum at about -15.81515
fw <- function(x) 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
# optimization with fewer function evaluations compared to SANN
res1 <- optim_ARS(50, fw,lower = -50, upper=100)
# often not as good performance when upper and lower bounds are poor
res2 <- optim_ARS(50, fw, lower=-Inf,upper=Inf)
# Only integer values allowed
## Not run:
res_int <- optim_ARS(50, fw, allowed_values = seq(-50,100,by=1))
## End(Not run)
## Not run:
#plot of the function and solutions
require(graphics)
plot(fw, -50, 50, n = 1000, main = "Minimizing 'wild function'")
points(-15.81515, fw(-15.81515), pch = 16, col = "red", cex = 1)
points(res1$par, res1$ofv, pch = 16, col = "green", cex = 1)
points(res2$par, res2$ofv, pch = 16, col = "blue", cex = 1)
## End(Not run)
# optim_ARS does not work great for hard to find minima on flat surface:
# Rosenbrock Banana function
# f(x, y) = (a-x)^2 + b(y-x^2)^2
# global minimum at (x, y)=(a, a^2), where f(x, y)=0.
# Usually a = 1 and b = 100.
## Not run:
fr <- function(x,a=1,b=100) {
x1 <- x[1]
x2 <- x[2]
b*(x2 - x1*x1)^2 + (a - x1)^2
}
res3 <- optim_ARS(c(-1.2,1), fr,lower = -5, upper = 5)
# plot the surface
x <- seq(-50, 50, length= 30)
y <- x
f <- function(x,y){apply(cbind(x,y),1,fr)}
z <- outer(x, y, f)
persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue", ticktype="detailed") -> res
points(trans3d(1, 1, 0, pmat = res), col = 2, pch = 16,cex=2)
points(trans3d(res3$par[1], res3$par[1], res3$ofv, pmat = res), col = "green", pch = 16,cex=2)
## End(Not run)
# box constraints
flb <- function(x){
p <- length(x)
sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2)
}
## 25-dimensional box constrained
#optim(rep(3, 25), flb,lower = rep(2, 25), upper = rep(4, 25),method = "L-BFGS-B")
res_box <- optim_ARS(rep(3, 25), flb,lower = rep(2, 25), upper = rep(4, 25))
## Combinatorial optimization: Traveling salesman problem
eurodistmat <- as.matrix(eurodist)
distance <- function(sq) { # Target function
sq2 <- embed(sq, 2)
sum(eurodistmat[cbind(sq2[,2], sq2[,1])])
}
genseq <- function(sq) { # Generate new candidate sequence
idx <- seq(2, NROW(eurodistmat)-1)
changepoints <- sample(idx, size = 2, replace = FALSE)
tmp <- sq[changepoints[1]]
sq[changepoints[1]] <- sq[changepoints[2]]
sq[changepoints[2]] <- tmp
sq
}
sq <- c(1:nrow(eurodistmat), 1) # Initial sequence: alphabetic
res3 <- optim_ARS(sq,distance,generator=genseq) # Near optimum distance around 12842
## Not run:
# plot of initial sequence
# rotate for conventional orientation
loc <- -cmdscale(eurodist, add = TRUE)$points
x <- loc[,1]; y <- loc[,2]
s <- seq_len(nrow(eurodistmat))
tspinit <- loc[sq,]
plot(x, y, type = "n", asp = 1, xlab = "", ylab = "",
main = paste("Initial sequence of traveling salesman problem\n",
"Distance =",distance(sq)), axes = FALSE)
arrows(tspinit[s,1], tspinit[s,2], tspinit[s+1,1], tspinit[s+1,2],
angle = 10, col = "green")
text(x, y, labels(eurodist), cex = 0.8)
# plot of final sequence from optim_ARS
tspres <- loc[res3$par,]
plot(x, y, type = "n", asp = 1, xlab = "", ylab = "",
main = paste("optim_ARS() 'solving' traveling salesman problem\n",
"Distance =",distance(c(1,res3$par,1))),axes = FALSE)
arrows(tspres[s,1], tspres[s,2], tspres[s+1,1], tspres[s+1,2],
angle = 10, col = "red")
text(x, y, labels(eurodist), cex = 0.8)
# using optim
set.seed(123) # chosen to get a good soln relatively quickly
(res4 <- optim(sq, distance, genseq, method = "SANN",
control = list(maxit = 30000, temp = 2000, trace = TRUE,
REPORT = 500)))
tspres <- loc[res4$par,]
plot(x, y, type = "n", asp = 1, xlab = "", ylab = "",
main = paste("optim() 'solving' traveling salesman problem\n",
"Distance =",distance(res4$par)),axes = FALSE)
arrows(tspres[s,1], tspres[s,2], tspres[s+1,1], tspres[s+1,2],
angle = 10, col = "red")
text(x, y, labels(eurodist), cex = 0.8)
## End(Not run)
# one-dimensional function
## Not run:
f <- function(x) abs(x)+cos(x)
res5 <- optim_ARS(-20,f,lower=-20, upper=20)
curve(f, -20, 20)
abline(v = res5$par, lty = 4,col="green")
## End(Not run)
# one-dimensional function
f <- function(x) (x^2+x)*cos(x) # -10 < x < 10
res_max <- optim_ARS(0,f,lower=-10, upper=10,maximize=TRUE) # sometimes to local maxima
## Not run:
res_min <- optim_ARS(0,f,lower=-10, upper=10) # sometimes to local minima
curve(f, -10, 10)
abline(v = res_min$par, lty = 4,col="green")
abline(v = res_max$par, lty = 4,col="red")
## End(Not run)
# two-dimensional Rastrigin function
#It has a global minimum at f(x) = f(0) = 0.
## Not run:
Rastrigin <- function(x1, x2){
20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
}
x1 <- x2 <- seq(-5.12, 5.12, by = 0.1)
z <- outer(x1, x2, Rastrigin)
res6 <- optim_ARS(c(-4,4),function(x) Rastrigin(x[1], x[2]),lower=-5.12, upper=5.12)
# color scale
nrz <- nrow(z)
ncz <- ncol(z)
jet.colors <-
colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
# Generate the desired number of colors from this palette
nbcol <- 100
color <- jet.colors(nbcol)
# Compute the z-value at the facet centres
zfacet <- z[-1, -1] + z[-1, -ncz] + z[-nrz, -1] + z[-nrz, -ncz]
# Recode facet z-values into color indices
facetcol <- cut(zfacet, nbcol)
persp(x1, x2, z, col = color[facetcol], phi = 30, theta = 30)
filled.contour(x1, x2, z, color.palette = jet.colors)
## End(Not run)
## Parallel computation
## works better when each evaluation takes longer
## here we have added extra time to the computations
## just to show that it works
## Not run:
res7 <- optim_ARS(c(-4,4),function(x){Sys.sleep(0.01); Rastrigin(x[1], x[2])},
lower=-5.12, upper=5.12)
res8 <- optim_ARS(c(-4,4),function(x){Sys.sleep(0.01); Rastrigin(x[1], x[2])},
lower=-5.12, upper=5.12,parallel = T)
res9 <- optim_ARS(c(-4,4),function(x){Sys.sleep(0.01); Rastrigin(x[1], x[2])},
lower=-5.12, upper=5.12,parallel = T,parallel_type = "snow")
## End(Not run)
Optimize a function using a line search algorithm.
Description
optim_LS performs sequential grid search optimization of an arbitrary function with respect
to each of the parameters to be optimized over.
The function works for both discrete and continuous optimization parameters
and allows for box-constraints (by using the upper and lower function arguments) or sets of allowed values (by using the
allowed_values function argument) for all parameters, or on a parameter per parameter basis.
Usage
optim_LS(
par,
fn,
lower = NULL,
upper = NULL,
allowed_values = NULL,
line_length = 50,
trace = TRUE,
maximize = F,
parallel = F,
parallel_type = NULL,
num_cores = NULL,
mrgsolve_model = NULL,
seed = round(runif(1, 0, 1e+07)),
allow_replicates = TRUE,
replicates_index = seq(1, length(par)),
ofv_initial = NULL,
closed_bounds = TRUE,
...
)
Arguments
par |
A vector of initial values for the parameters to be optimized over. |
fn |
A function to be minimized (or maximized), with first argument the vector of parameters over which minimization is to take place. It should return a scalar result. |
lower |
Lower bounds on the parameters. A vector. |
upper |
Upper bounds on the parameters. A vector. |
allowed_values |
A list containing allowed values for each parameter |
line_length |
The number of different parameter values per parameter to evaluate. The values are selected as an evenly spaced grid between the upper and lower bounds. |
trace |
Should the algorithm output results intermittently. |
maximize |
Should the function be maximized? Default is to minimize. |
parallel |
Should we use parallel computations? |
parallel_type |
Which type of parallelization should be used?
Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on
Linux-like systems. By default this is chosen for you depending on your operating system.
See |
num_cores |
The number of cores to use in the parallelization. By default is set to the number
output from
|
mrgsolve_model |
If the computations require a mrgsolve model and you
are using the "snow" method then you need to specify the name of the model
object created by |
seed |
The random seed to use in the algorithm, |
allow_replicates |
Should the algorithm allow parameters to have the same value? |
replicates_index |
A vector, the same length as the parameters. If two values are the same in this vector then the parameters may not assume the same value in the optimization. |
ofv_initial |
An initial objective function value (OFV). If not NULL then the initial design is not evaluated and the OFV value is assumed to be this number. |
closed_bounds |
Are the upper and lower limits open (boundaries not allowed) or closed (boundaries allowed) bounds? |
... |
Additional arguments passed to |
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
# "wild" function
fw <- function(x) 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
# Global minimum of 67.47 at about -15.81515
(fw_min <- fw(-15.81515))
if (interactive()){
#plot of the function
require(graphics)
plot(fw, -50, 50, n = 10000, main = "Minimizing 'wild function'")
# Known minimum
points(-15.81515, fw_min, pch = 21, col = "red", cex = 1.5)
}
# optimization with fewer function evaluations
# compared to SANN: see examples in '?optim'
res1 <- optim_LS(50, fw,lower = -50, upper=50, line_length = 10000)
if (interactive()){
require(graphics)
plot(fw, -20, 0, n = 10000, main = "Minimizing 'wild function'")
# Known minimum
points(-15.81515, fw_min, pch = 21, col = "red", cex = 1.5)
#plot of the optimization
points(res1$par, res1$ofv, pch = 16, col = "green", cex = 1)
}
# Upper and lower bounds and line_length should be considered carefully
res2 <- optim_LS(50, fw, lower=-Inf,upper=Inf,line_length = 10000)
# Only integer values allowed
res_int <- optim_LS(50, fw, allowed_values = seq(-50,50,by=1))
# Rosenbrock Banana function
# f(x, y) = (a-x)^2 + b*(y-x^2)^2
# global minimum at (x, y)=(a, a^2), where f(x, y)=0.
# Usually a = 1 and b = 100 so x=1 and y=1
if (interactive()){
fr <- function(x,a=1,b=100) {
x1 <- x[1]
x2 <- x[2]
b*(x2 - x1*x1)^2 + (a - x1)^2
}
res3 <- optim_LS(c(-1.2,1), fr,lower = -5, upper = 5, line_length = 1000)
# plot the surface
x <- seq(-50, 50, length= 30)
y <- x
f <- function(x,y){apply(cbind(x,y),1,fr)}
z <- outer(x, y, f)
persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue", ticktype="detailed") -> res
points(trans3d(1, 1, 0, pmat = res), col = 2, pch = 16,cex=2)
points(trans3d(res3$par[1], res3$par[1], res3$ofv, pmat = res), col = "green", pch = 16,cex=1.5)
}
# box constraints
flb <- function(x){
p <- length(x)
sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2)
}
## 25-dimensional box constrained
if (interactive()){
optim(rep(3, 25), flb,lower = rep(2, 25), upper = rep(4, 25),method = "L-BFGS-B")
}
res_box <- optim_LS(rep(3, 25), flb,
lower = rep(2, 25),
upper = rep(4, 25),
line_length = 1000)
# one-dimensional function
if (interactive()){
f <- function(x) abs(x)+cos(x)
res5 <- optim_LS(-20,f,lower=-20, upper=20, line_length = 500)
curve(f, -20, 20)
abline(v = res5$par, lty = 4,col="green")
}
# one-dimensional function
f <- function(x) (x^2+x)*cos(x) # -10 < x < 10
res_max <- optim_LS(0,f,lower=-10, upper=10,maximize=TRUE,line_length = 1000)
if (interactive()){
res_min <- optim_LS(0,f,lower=-10, upper=10, line_length = 1000)
curve(f, -10, 10)
abline(v = res_min$par, lty = 4,col="green")
abline(v = res_max$par, lty = 4,col="red")
}
# two-dimensional Rastrigin function
#It has a global minimum at f(x) = f(0) = 0.
if (interactive()){
Rastrigin <- function(x1, x2){
20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
}
x1 <- x2 <- seq(-5.12, 5.12, by = 0.1)
z <- outer(x1, x2, Rastrigin)
res6 <- optim_LS(c(-4,4),function(x) Rastrigin(x[1], x[2]),
lower=-5.12, upper=5.12, line_length = 1000)
# color scale
nrz <- nrow(z)
ncz <- ncol(z)
jet.colors <-
colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
# Generate the desired number of colors from this palette
nbcol <- 100
color <- jet.colors(nbcol)
# Compute the z-value at the facet centres
zfacet <- z[-1, -1] + z[-1, -ncz] + z[-nrz, -1] + z[-nrz, -ncz]
# Recode facet z-values into color indices
facetcol <- cut(zfacet, nbcol)
persp(x1, x2, z, col = color[facetcol], phi = 30, theta = 30)
filled.contour(x1, x2, z, color.palette = jet.colors)
}
## Parallel computation
## works better when each evaluation takes longer
## here we have added extra time to the computations
## just to show that it works
if (interactive()){
res7 <- optim_LS(c(-4,4),function(x){Sys.sleep(0.01); Rastrigin(x[1], x[2])},
lower=-5.12, upper=5.12, line_length = 200)
res8 <- optim_LS(c(-4,4),function(x){Sys.sleep(0.01); Rastrigin(x[1], x[2])},
lower=-5.12, upper=5.12, line_length = 200, parallel = TRUE)
res9 <- optim_LS(c(-4,4),function(x){Sys.sleep(0.01); Rastrigin(x[1], x[2])},
lower=-5.12, upper=5.12, line_length = 200, parallel = TRUE,
parallel_type = "snow")
}
Title Optimize the proportion of individuals in the design groups
Description
Title Optimize the proportion of individuals in the design groups
Usage
optimize_groupsize(
poped.db,
props = c(poped.db$design$groupsize/sum(poped.db$design$groupsize)),
trace = 1,
...
)
Arguments
poped.db |
A PopED database. |
props |
The proportions of individuals in each group (relative to the total number of individuals) to start the optimization from. |
trace |
Should there be tracing of the optimization? Value can be integer values. Larger numbers give more information. |
... |
Value
A list of the initial objective function value, optimal proportions, the objective function value with those proportions, the optimal number of individuals in each group (with integer number of individuals), and the objective function value with that number of individuals.
Examples
# 2 design groups with either early or late samples
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
},
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
xt=list(c(1,2,3),c(4,5,20,120)),
groupsize=50,
minxt=0.01,
maxxt=120,
a=70,
mina=0.01,
maxa=100)
plot_model_prediction(poped.db)
evaluate_design(poped.db)
# what are the optimal proportions of
# individuals in the two groups in the study?
(n_opt <- optimize_groupsize(poped.db))
# How many individuals in the original design are needed to achieve an
# efficiency of 1 compared to the optimized design with n=100?
optimize_n_eff(poped.db,
ofv_ref=n_opt$opt_ofv_with_n)
Translate efficiency to number of subjects
Description
optimize HOW MANY n there should be to achieve efficiency=1 compared to a reference OFV
Usage
optimize_n_eff(poped.db, ofv_ref, norm_group_fim = NULL, ...)
Arguments
poped.db |
A PopED database. |
ofv_ref |
A reference OFV value to compare to. |
norm_group_fim |
The FIM per individual in each design group. If |
... |
Arguments passed to |
Value
The number of individuals needed.
Examples
# 2 design groups with either early or late samples
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
},
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
xt=list(c(1,2,3),c(4,5,20,120)),
groupsize=50,
minxt=0.01,
maxxt=120,
a=70,
mina=0.01,
maxa=100)
plot_model_prediction(poped.db)
evaluate_design(poped.db)
# what are the optimal proportions of
# individuals in the two groups in the study?
(n_opt <- optimize_groupsize(poped.db))
# How many individuals in the original design are needed to achieve an
# efficiency of 1 compared to the optimized design with n=100?
optimize_n_eff(poped.db,
ofv_ref=n_opt$opt_ofv_with_n)
Optimize the number of subjects based on desired uncertainty of a parameter.
Description
Optimize the number of subjects, based on the current design and the desired uncertainty of a single parameter
Usage
optimize_n_rse(
poped.db,
bpop_idx,
need_rse,
use_percent = TRUE,
allowed_values = seq(poped.db$design$m, sum(poped.db$design$groupsize) * 5, by =
poped.db$design$m)
)
Arguments
poped.db |
A PopED database. |
bpop_idx |
The index number of the parameter, currently only bpop parameters are allowed. |
need_rse |
The relative standard error (RSE) one would like to achieve (in percent, by default). |
use_percent |
Should the RSE be represented as a percentage (T/F)? |
allowed_values |
A vector of the allowed total number of subjects in the study. |
Value
The total number of subjects needed and the RSE of the parameter.
Examples
# 2 design groups with either early or late samples
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
},
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
xt=list(c(1,2,3),c(4,5,20,120)),
groupsize=50,
minxt=0.01,
maxxt=120,
a=70,
mina=0.01,
maxa=100)
# plot of the design
plot_model_prediction(poped.db)
# the current RSE values
evaluate_design(poped.db)$rse
# number of individuals if CL should have 10% RSE
optimize_n_rse(poped.db,
bpop_idx=1, # for CL
need_rse=10) # the RSE you want
Parameter simulation
Description
Function generates random samples for a list of parameters
Usage
pargen(par, user_dist_pointer, sample_size, bLHS, sample_number, poped.db)
Arguments
par |
A matrix describing the parameters. Each row is a parameter and the matrix has three columns:
|
user_dist_pointer |
A text string of the name of a function that generates random samples from a user defined distribution. |
sample_size |
The number of random samples per parameter to generate |
bLHS |
Logical, indicating if Latin Hypercube Sampling should be used. |
sample_number |
The sample number to extract from a user distribution. |
poped.db |
A PopED database. |
Value
A matrix of random samples of size (sample_size x number_of_parameters)
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
# Adding 40% Uncertainty to fixed effects log-normal (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.4)^2) # 40% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
pars.ln <- pargen(par=bpop_vals_ed_ln,
user_dist_pointer=NULL,
sample_size=1000,
bLHS=1,
sample_number=NULL,
poped.db)
# Adding 10% Uncertainty to fixed effects normal-distribution (not Favail)
bpop_vals_ed_n <- cbind(ones(length(bpop_vals),1)*1, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_n["Favail",] <- c(0,1,0)
pars.n <- pargen(par=bpop_vals_ed_n,
user_dist_pointer=NULL,
sample_size=1000,
bLHS=1,
sample_number=NULL,
poped.db)
# Adding 10% Uncertainty to fixed effects uniform-distribution (not Favail)
bpop_vals_ed_u <- cbind(ones(length(bpop_vals),1)*2, # uniform distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)) # 10% of bpop value
bpop_vals_ed_u["Favail",] <- c(0,1,0)
pars.u <- pargen(par=bpop_vals_ed_u,
user_dist_pointer=NULL,
sample_size=1000,
bLHS=1,
sample_number=NULL,
poped.db)
# Adding user defined distributions
bpop_vals_ed_ud <- cbind(ones(length(bpop_vals),1)*3, # user dfined distribution
bpop_vals,
bpop_vals*0.1) # 10% of bpop value
bpop_vals_ed_ud["Favail",] <- c(0,1,0)
# A normal distribution
my_dist <- function(...){
par_vec <- rnorm(c(1,1,1,1),mean=bpop_vals_ed_ud[,2],sd=bpop_vals_ed_ud[,3])
}
pars.ud <- pargen(par=bpop_vals_ed_ud,
user_dist_pointer=my_dist,
sample_size=1000,
bLHS=1,
sample_number=NULL,
poped.db)
Plot the efficiency of windows
Description
Function plots the efficiency of windows around the sample time points.
The function samples from a uniform distribution around the sample time
points for each group (or each individual with deviate_by_id=TRUE,
with slower calculation times) and compares the results with the
design defined in poped.db. The maximal and minimal allowed values for all design variables as
defined in poped.db are respected (e.g. poped.db$design_space$minxt and
poped.db$design_space$maxxt).
Usage
plot_efficiency_of_windows(
poped.db,
xt_windows = NULL,
xt_plus = xt_windows,
xt_minus = xt_windows,
iNumSimulations = 100,
y_eff = TRUE,
y_rse = TRUE,
ofv_calc_type = poped.db$settings$ofv_calc_type,
mean_line = TRUE,
mean_color = "red",
deviate_by_id = FALSE,
parallel = F,
seed = round(runif(1, 0, 1e+07)),
...
)
Arguments
poped.db |
A poped database |
xt_windows |
The distance on one direction from the optimal sample
times. Can be a number or a matrix of the same size as the xt matrix found
in |
xt_plus |
The upper distance from the optimal sample times (xt +
xt_plus). Can be a number or a matrix of the same size as the xt matrix
found in |
xt_minus |
The lower distance from the optimal sample times (xt -
xt_minus). Can be a number or a matrix of the same size as the xt matrix
found in |
iNumSimulations |
The number of design simulations to make within the specified windows. |
y_eff |
Should one of the plots created have efficiency on the y-axis? |
y_rse |
Should created plots include the relative standard error of each parameter as a value on the y-axis? |
ofv_calc_type |
OFV calculation type for FIM
|
mean_line |
Should a mean value line be created? |
mean_color |
The color of the mean value line. |
deviate_by_id |
Should the computations look at deviations per individual instead of per group? |
parallel |
Should we use parallel computations (T/F)?
Other options can be defined in this function and passed
to |
seed |
The random seed to use. |
... |
Extra arguments passed to |
Value
A ggplot object.
See Also
Other evaluate_design:
evaluate.fim(),
evaluate_design(),
evaluate_power(),
get_rse(),
model_prediction(),
plot_model_prediction()
Other Simulation:
model_prediction(),
plot_model_prediction()
Other Graphics:
plot_model_prediction()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
# Examine efficiency of sampling windows at plus/minus 0.5 hours from
# sample points in the design
plot_efficiency_of_windows(poped.db,xt_windows=0.5)
if(interactive()){
plot_efficiency_of_windows(poped.db,
xt_plus=c( 0.5,1,2,1,2,3,7,1),
xt_minus=c( 0.1,2,5,4,2,3,6,2))
plot_efficiency_of_windows(poped.db,xt_windows=c( 0.5,1,2,1,2,3,7,1))
plot_efficiency_of_windows(poped.db,
xt_plus=c( 0.5,1,2,1,2,3,7,1),
xt_minus=c( 0.1,2,5,4,2,3,6,2),
y_rse=FALSE)
plot_efficiency_of_windows(poped.db,
xt_plus=c( 0.5,1,2,1,2,3,7,1),
xt_minus=c( 0.1,2,5,4,2,3,6,2),
y_eff=FALSE)
}
Plot model predictions
Description
Function plots model predictions for the typical value in the population, individual predictions and data predictions.
Usage
plot_model_prediction(
poped.db,
model_num_points = 100,
groupsize_sim = 100,
separate.groups = F,
sample.times = T,
sample.times.IPRED = F,
sample.times.DV = F,
PRED = T,
IPRED = F,
IPRED.lines = F,
IPRED.lines.pctls = F,
alpha.IPRED.lines = 0.1,
alpha.IPRED = 0.3,
sample.times.size = 4,
DV = F,
alpha.DV = 0.3,
DV.lines = F,
DV.points = F,
alpha.DV.lines = 0.3,
alpha.DV.points = 0.3,
sample.times.DV.points = F,
sample.times.DV.lines = F,
alpha.sample.times.DV.points = 0.3,
alpha.sample.times.DV.lines = 0.3,
y_lab = "Model Predictions",
facet_scales = "fixed",
facet_label_names = T,
model.names = NULL,
DV.mean.sd = FALSE,
PI = FALSE,
PI_alpha = 0.3,
...
)
Arguments
poped.db |
A PopED database. |
model_num_points |
How many extra observation rows should be created in the data frame for each group or individual
per model. If used then the points are placed evenly between |
groupsize_sim |
How many individuals per group should be simulated when DV=TRUE or IPRED=TRUE to create prediction intervals? |
separate.groups |
Should there be separate plots for each group. |
sample.times |
Should sample times be shown on the plots. |
sample.times.IPRED |
Should sample times be shown based on the IPRED y-values. |
sample.times.DV |
Should sample times be shown based on the DV y-values. |
PRED |
Should a PRED line be drawn. |
IPRED |
Should we simulate individual predictions? |
IPRED.lines |
Should IPRED lines be drawn? |
IPRED.lines.pctls |
Should lines be drawn at the chosen percentiles of the IPRED values? |
alpha.IPRED.lines |
What should the transparency for the IPRED.lines be? |
alpha.IPRED |
What should the transparency of the IPRED CI? |
sample.times.size |
What should the size of the sample.times be? |
DV |
should we simulate observations? |
alpha.DV |
What should the transparency of the DV CI? |
DV.lines |
Should DV lines be drawn? |
DV.points |
Should DV points be drawn? |
alpha.DV.lines |
What should the transparency for the DV.lines be? |
alpha.DV.points |
What should the transparency for the DV.points be? |
sample.times.DV.points |
TRUE or FALSE. |
sample.times.DV.lines |
TRUE or FALSE. |
alpha.sample.times.DV.points |
What should the transparency for the sample.times.DV.points be? |
alpha.sample.times.DV.lines |
What should the transparency for the sample.times.DV.lines be? |
y_lab |
The label of the y-axis. |
facet_scales |
Can be "free", "fixed", "free_x" or "free_y" |
facet_label_names |
TRUE or FALSE |
model.names |
A vector of names of the response model/s (the length of the vector should be equal to the number of response models). It is Null by default. |
DV.mean.sd |
Plot the mean and standard deviation of simulated observations. |
PI |
Plot prediction intervals for the expected data given the model.
Predictions are based on first-order approximations to
the model variance and a normality assumption of that variance. As such these computations are
more approximate than using |
PI_alpha |
The transparency of the PI. |
... |
Additional arguments passed to the |
Value
A ggplot object. If you would like to further edit this plot don't
forget to load the ggplot2 library using library(ggplot2).
See Also
Other evaluate_design:
evaluate.fim(),
evaluate_design(),
evaluate_power(),
get_rse(),
model_prediction(),
plot_efficiency_of_windows()
Other Simulation:
model_prediction(),
plot_efficiency_of_windows()
Other Graphics:
plot_efficiency_of_windows()
Examples
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## create plot of model without variability
plot_model_prediction(poped.db)
## create plot of model with variability by simulating from OMEGA and SIGMA
plot_model_prediction(poped.db,IPRED=TRUE,DV=TRUE)
## create plot of model with variability by
## computing the expected variance (using an FO approximation)
## and then computing a prediction interval
## based on an assumption of normality
## computation is faster but less accurate
## compared to using DV=TRUE (and groupsize_sim = 500)
plot_model_prediction(poped.db,PI=TRUE)
##-- Model: One comp first order absorption + inhibitory imax
## -- works for both mutiple and single dosing
ff <- function(model_switch,xt,parameters,poped.db){
with(as.list(parameters),{
y=xt
MS <- model_switch
# PK model
N = floor(xt/TAU)+1
CONC=(DOSE*Favail/V)*(KA/(KA - CL/V)) *
(exp(-CL/V * (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) -
exp(-KA * (xt - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
# PD model
EFF = E0*(1 - CONC*IMAX/(IC50 + CONC))
y[MS==1] = CONC[MS==1]
y[MS==2] = EFF[MS==2]
return(list( y= y,poped.db=poped.db))
})
}
## -- parameter definition function
sfg <- function(x,a,bpop,b,bocc){
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU = a[2],
E0=bpop[5]*exp(b[4]),
IMAX=bpop[6],
IC50=bpop[7])
return( parameters )
}
## -- Residual Error function
feps <- function(model_switch,xt,parameters,epsi,poped.db){
returnArgs <- ff(model_switch,xt,parameters,poped.db)
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
MS <- model_switch
pk.dv <- y*(1+epsi[,1])+epsi[,2]
pd.dv <- y*(1+epsi[,3])+epsi[,4]
y[MS==1] = pk.dv[MS==1]
y[MS==2] = pd.dv[MS==2]
return(list( y= y,poped.db =poped.db ))
}
poped.db <-
create.poped.database(
ff_fun=ff,
fError_fun=feps,
fg_fun=sfg,
groupsize=20,
m=3,
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9,
E0=1120,IMAX=0.807,IC50=0.0993),
notfixed_bpop=c(1,1,1,0,1,1,1),
d=c(V=0.09,KA=0.09,CL=0.25^2,E0=0.09),
sigma=c(0.04,5e-6,0.09,100),
notfixed_sigma=c(0,0,0,0),
xt=c( 1,2,8,240,240,1,2,8,240,240),
minxt=c(0,0,0,240,240,0,0,0,240,240),
maxxt=c(10,10,10,248,248,10,10,10,248,248),
discrete_xt = list(0:248),
G_xt=c(1,2,3,4,5,1,2,3,4,5),
bUseGrouped_xt=1,
model_switch=c(1,1,1,1,1,2,2,2,2,2),
a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24),c(DOSE=0, TAU=24)),
maxa=c(DOSE=200,TAU=40),
mina=c(DOSE=0,TAU=2),
ourzero=0)
## create plot of model and design
plot_model_prediction(poped.db,facet_scales="free",
model.names = c("PK","PD"))
## create plot of model with variability by
## computing the expected variance (using an FO approximation)
## and then computing a prediction interval
## based on an assumption of normality
## computation is faster but less accurate
## compared to using DV=TRUE (and groupsize_sim = 500)
plot_model_prediction(poped.db,facet_scales="free",
model.names = c("PK","PD"),
PI=TRUE,
separate.groups = TRUE)
Choose between arg1 and arg2
Description
Function chooses arg1 unless it is NULL in which case arg2 is chosen.
Usage
poped.choose(arg1, arg2)
Arguments
arg1 |
The first argument |
arg2 |
The second argument |
See Also
Other poped_input:
convert_variables(),
create.poped.database(),
create_design(),
create_design_space(),
downsizing_general_design()
Examples
poped.choose(2,5)
poped.choose("foo",66)
poped.choose(NULL,"hello")
Run the graphical interface for PopED
Description
Run the graphical interface for PopED
Usage
poped_gui()
Optimize a design defined in a PopED database
Description
Optimize a design defined in a PopED database using the objective function described in the database (or in the arguments to this function). The function works for both discrete and continuous optimization variables.
Usage
poped_optim(
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
method = c("ARS", "BFGS", "LS"),
control = list(),
trace = TRUE,
fim.calc.type = poped.db$settings$iFIMCalculationType,
ofv_calc_type = poped.db$settings$ofv_calc_type,
ds_index = poped.db$parameters$ds_index,
approx_type = poped.db$settings$iApproximationMethod,
d_switch = poped.db$settings$d_switch,
ED_samp_size = poped.db$settings$ED_samp_size,
bLHS = poped.db$settings$bLHS,
use_laplace = poped.db$settings$iEDCalculationType,
out_file = "",
parallel = F,
parallel_type = NULL,
num_cores = NULL,
mrgsolve_model = NULL,
loop_methods = ifelse(length(method) > 1, TRUE, FALSE),
iter_max = 10,
stop_crit_eff = 1.001,
stop_crit_diff = NULL,
stop_crit_rel = NULL,
ofv_fun = poped.db$settings$ofv_fun,
maximize = T,
allow_replicates = TRUE,
allow_replicates_xt = TRUE,
allow_replicates_a = TRUE,
...
)
Arguments
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
method |
A vector of optimization methods to use in a sequential
fashion. Options are |
control |
Contains control arguments for each method specified. |
trace |
Should the algorithm output results intermittently. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
ofv_calc_type |
OFV calculation type for FIM
|
ds_index |
Ds_index is a vector set to 1 if a parameter is uninteresting, otherwise 0.
size=(1,num unfixed parameters). First unfixed bpop, then unfixed d, then unfixed docc and last unfixed sigma.
Default is the fixed effects being important, everything else not important. Used in conjunction with
|
approx_type |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI. |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
out_file |
Save output from the optimization to a file. |
parallel |
Should we use parallel computations? |
parallel_type |
Which type of parallelization should be used?
Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on
Linux-like systems. By default this is chosen for you depending on your operating system.
See |
num_cores |
The number of cores to use in the parallelization. By default is set to the number
output from
|
mrgsolve_model |
If the computations require a mrgsolve model and you
are using the "snow" method then you need to specify the name of the model
object created by |
loop_methods |
Should the optimization methods be looped for
|
iter_max |
If line search is used then the algorithm tests if line
search (always run at the end of the optimization iteration) changes the
design in any way. If not, the algorithm stops. If yes, then a new
iteration is run unless |
stop_crit_eff |
If |
stop_crit_diff |
If |
stop_crit_rel |
If |
ofv_fun |
User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it. |
maximize |
Should the objective function be maximized or minimized? |
allow_replicates |
Should the algorithm allow optimized design components to have the same value? If FALSE then
all discrete optimizations will not allow replicates within variable types
(equivalent to |
allow_replicates_xt |
Should the algorithm allow optimized |
allow_replicates_a |
Should the algorithm allow optimized |
... |
arguments passed to other functions. |
Details
This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
If more than one optimization method is
specified then the methods are run in series. If loop_methods=TRUE
then the series of optimization methods will be run for iter_max
iterations, or until the efficiency of the design after the current series
(compared to the start of the series) is less than stop_crit_eff.
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# D-family Optimization
##############
# below are a number of ways to optimize the problem
# ARS+BFGS+LS optimization of dose
# optimization with just a few iterations
# only to check that things are working
out_1 <- poped_optim(poped.db,opt_a =TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
# cost function
# PRED at 120 hours
crit_fcn <- function(poped.db,...){
pred_df <- model_prediction(poped.db)
return(pred_df[pred_df$Time==120,"PRED"])
}
# maximize cost function
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 2)
# minimize the cost function
out_3 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 2,
maximize = FALSE,
evaluate_fim = FALSE)
## Not run:
# RS+BFGS+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optim(poped.db,opt_xt=T,parallel = TRUE)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db)
# optimization with only integer times allowed
poped.db.2 <- poped.db
poped.db.2$design_space$xt_space <- matrix(list(seq(1,120)),1,8)
output_2 <- poped_optim(poped.db.2,opt_xt=T,parallel = TRUE)
get_rse(output_2$FIM,output_2$poped.db)
plot_model_prediction(output_2$poped.db)
# Examine efficiency of sampling windows
plot_efficiency_of_windows(output_2$poped.db,xt_windows=0.5)
plot_efficiency_of_windows(output_2$poped.db,xt_windows=1)
# Adaptive Random Search (ARS, just a few samples here)
rs.output <- poped_optim(poped.db,opt_xt=T,method = "ARS",
control = list(ARS=list(iter=5)))
get_rse(rs.output$FIM,rs.output$poped.db)
# line search, DOSE and sample time optimization
ls.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "LS",
control = list(LS=list(line_length=5)))
# Adaptive random search,
# DOSE and sample time optimization
ars.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "ARS",
control = list(ARS=list(iter=5)))
# BFGS gradient search from the stats::optim() function,
# DOSE and sample time optimization
bfgs.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "BFGS",
control = list(BFGS=list(maxit=5)))
# genetic algorithm from the GA::ga() function,
# DOSE and sample time optimization
ga.output <- poped_optim(poped.db,opt_xt=T,opt_a=F,method = "GA",parallel=T)
# cost function with GA
# maximize
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
parallel = T,
method=c("GA"))
# cost function with GA
# minimize
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
parallel = T,
method=c("GA"),
iter_max = 1,
maximize = F,
evaluate_fim = F)
# optimize distribution of individuals in 3 groups
poped_db_2 <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
m=3,
xt=list(c( 0.5,1,2,6,8),c(36,72,120),
c(10,12,14,16,18,20,22,24)),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
opt_xt_inds <-
poped_optim(poped_db_2,
opt_a =TRUE,
opt_inds = TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
##############
# E-family Optimization
##############
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
# E_ln(D) optimization using Random search (just a few samples here)
output <- poped_optim(poped.db,opt_xt=TRUE,opt_a=TRUE,d_switch=0,
method = c("ARS","LS"),
control = list(ARS=list(iter=2),
LS=list(line_length=2)),
iter_max = 1)
get_rse(output$FIM,output$poped.db)
# ED with laplace approximation,
# optimization using Random search (just a few iterations here)
ars.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "ARS",
d_switch=0,use_laplace=TRUE,#laplace.fim=TRUE,
parallel=T,
control = list(ARS=list(iter=5)))
## End(Not run)
Optimization main module for PopED
Optimize the objective function. The function works for both discrete and
continuous optimization variables. If more than one optimization method is
specified then the methods are run in series. If loop_methods=TRUE
then the series of optimization methods will be run for iter_max
iterations, or until the efficiency of the design after the current series
(compared to the start of the series) is less than stop_crit_eff.
Description
This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
Usage
poped_optim_1(
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
method = c("ARS", "BFGS", "LS"),
control = list(),
trace = TRUE,
fim.calc.type = poped.db$settings$iFIMCalculationType,
ofv_calc_type = poped.db$settings$ofv_calc_type,
approx_type = poped.db$settings$iApproximationMethod,
d_switch = poped.db$settings$d_switch,
ED_samp_size = poped.db$settings$ED_samp_size,
bLHS = poped.db$settings$bLHS,
use_laplace = poped.db$settings$iEDCalculationType,
out_file = "",
parallel = F,
parallel_type = NULL,
num_cores = NULL,
loop_methods = ifelse(length(method) > 1, TRUE, FALSE),
iter_max = 10,
stop_crit_eff = 1.001,
stop_crit_diff = NULL,
stop_crit_rel = NULL,
ofv_fun = poped.db$settings$ofv_fun,
maximize = T,
...
)
Arguments
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
method |
A vector of optimization methods to use in a sequential
fashion. Options are |
control |
Contains control arguments for each method specified. |
trace |
Should the algorithm output results intermittently. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
ofv_calc_type |
OFV calculation type for FIM
|
approx_type |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI. |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
out_file |
Save output from the optimization to a file. |
parallel |
Should we use parallel computations? |
parallel_type |
Which type of parallelization should be used?
Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on
Linux-like systems. By default this is chosen for you depending on your operating system.
See |
num_cores |
The number of cores to use in the parallelization. By default is set to the number
output from
|
loop_methods |
Should the optimization methods be looped for
|
iter_max |
If line search is used then the algorithm tests if line
search (always run at the end of the optimization iteration) changes the
design in any way. If not, the algorithm stops. If yes, then a new
iteration is run unless |
stop_crit_eff |
If |
stop_crit_diff |
If |
stop_crit_rel |
If |
ofv_fun |
User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it. |
maximize |
Should the objective function be maximized or minimized? |
... |
arguments passed to other functions. |
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Optimization main module for PopED
Description
Optimize the objective function. The function works for both discrete and
continuous optimization variables. If more than one optimization method is
specified then the methods are run in series. If loop_methods=TRUE
then the series of optimization methods will be run for iter_max
iterations, or until the efficiency of the design after the current series
(compared to the start of the series) is less than stop_crit_eff.
Usage
poped_optim_2(
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
method = c("ARS", "BFGS", "LS"),
control = list(),
trace = TRUE,
fim.calc.type = poped.db$settings$iFIMCalculationType,
ofv_calc_type = poped.db$settings$ofv_calc_type,
approx_type = poped.db$settings$iApproximationMethod,
d_switch = poped.db$settings$d_switch,
ED_samp_size = poped.db$settings$ED_samp_size,
bLHS = poped.db$settings$bLHS,
use_laplace = poped.db$settings$iEDCalculationType,
out_file = "",
parallel = F,
parallel_type = NULL,
num_cores = NULL,
loop_methods = ifelse(length(method) > 1, TRUE, FALSE),
iter_max = 10,
stop_crit_eff = 1.001,
stop_crit_diff = NULL,
stop_crit_rel = NULL,
ofv_fun = poped.db$settings$ofv_fun,
maximize = T,
transform_parameters = F,
...
)
Arguments
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
method |
A vector of optimization methods to use in a sequential
fashion. Options are |
control |
Contains control arguments for each method specified. |
trace |
Should the algorithm output results intermittently. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
ofv_calc_type |
OFV calculation type for FIM
|
approx_type |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI. |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
out_file |
Save output from the optimization to a file. |
parallel |
Should we use parallel computations? |
parallel_type |
Which type of parallelization should be used?
Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on
Linux-like systems. By default this is chosen for you depending on your operating system.
See |
num_cores |
The number of cores to use in the parallelization. By default is set to the number
output from
|
loop_methods |
Should the optimization methods be looped for
|
iter_max |
If line search is used then the algorithm tests if line
search (always run at the end of the optimization iteration) changes the
design in any way. If not, the algorithm stops. If yes, then a new
iteration is run unless |
stop_crit_eff |
If |
stop_crit_diff |
If |
stop_crit_rel |
If |
ofv_fun |
User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it. |
maximize |
Should the objective function be maximized or minimized? |
transform_parameters |
Should we transform the parameters before optimization? |
... |
arguments passed to other functions. |
Details
This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_3(),
poped_optimize()
Optimization main module for PopED
Description
Optimize the objective function. The function works for both discrete and
continuous optimization variables. If more than one optimization method is
specified then the methods are run in series. If loop_methods=TRUE
then the series of optimization methods will be run for iter_max
iterations, or until the efficiency of the design after the current series
(compared to the start of the series) is less than stop_crit_eff.
Usage
poped_optim_3(
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
method = c("ARS", "BFGS", "LS"),
control = list(),
trace = TRUE,
fim.calc.type = poped.db$settings$iFIMCalculationType,
ofv_calc_type = poped.db$settings$ofv_calc_type,
ds_index = poped.db$parameters$ds_index,
approx_type = poped.db$settings$iApproximationMethod,
d_switch = poped.db$settings$d_switch,
ED_samp_size = poped.db$settings$ED_samp_size,
bLHS = poped.db$settings$bLHS,
use_laplace = poped.db$settings$iEDCalculationType,
out_file = "",
parallel = F,
parallel_type = NULL,
num_cores = NULL,
loop_methods = ifelse(length(method) > 1, TRUE, FALSE),
iter_max = 10,
stop_crit_eff = 1.001,
stop_crit_diff = NULL,
stop_crit_rel = NULL,
ofv_fun = poped.db$settings$ofv_fun,
maximize = T,
allow_replicates = TRUE,
allow_replicates_xt = TRUE,
allow_replicates_a = TRUE,
...
)
Arguments
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
method |
A vector of optimization methods to use in a sequential
fashion. Options are |
control |
Contains control arguments specified for each method separately. |
trace |
Should the algorithm output results intermittently. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
ofv_calc_type |
OFV calculation type for FIM
|
ds_index |
Ds_index is a vector set to 1 if a parameter is uninteresting, otherwise 0.
size=(1,num unfixed parameters). First unfixed bpop, then unfixed d, then unfixed docc and last unfixed sigma.
Default is the fixed effects being important, everything else not important. Used in conjunction with
|
approx_type |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI. |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
out_file |
Save output from the optimization to a file. |
parallel |
Should we use parallel computations? |
parallel_type |
Which type of parallelization should be used?
Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on
Linux-like systems. By default this is chosen for you depending on your operating system.
See |
num_cores |
The number of cores to use in the parallelization. By default is set to the number
output from
|
loop_methods |
Should the optimization methods be looped for
|
iter_max |
If line search is used then the algorithm tests if line
search (always run at the end of the optimization iteration) changes the
design in any way. If not, the algorithm stops. If yes, then a new
iteration is run unless |
stop_crit_eff |
If |
stop_crit_diff |
If |
stop_crit_rel |
If |
ofv_fun |
User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it. |
maximize |
Should the objective function be maximized or minimized? |
allow_replicates |
Should the algorithm allow parameters to have the same value? |
... |
arguments passed to other functions. |
Details
This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optimize()
Retired optimization module for PopED
Description
This function is an older version of poped_optim. Please use poped_optim
unless you have a specific reason to use this function instead.
Usage
poped_optimize(
poped.db,
ni = NULL,
xt = NULL,
model_switch = NULL,
x = NULL,
a = NULL,
bpop = NULL,
d = NULL,
maxxt = NULL,
minxt = NULL,
maxa = NULL,
mina = NULL,
fmf = 0,
dmf = 0,
trflag = TRUE,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
cfaxt = poped.db$settings$cfaxt,
cfaa = poped.db$settings$cfaa,
rsit = poped.db$settings$rsit,
rsit_output = poped.db$settings$rsit_output,
fim.calc.type = poped.db$settings$iFIMCalculationType,
ofv_calc_type = poped.db$settings$ofv_calc_type,
approx_type = poped.db$settings$iApproximationMethod,
bUseExchangeAlgorithm = poped.db$settings$bUseExchangeAlgorithm,
iter = 1,
d_switch = poped.db$settings$d_switch,
ED_samp_size = poped.db$settings$ED_samp_size,
bLHS = poped.db$settings$bLHS,
use_laplace = poped.db$settings$iEDCalculationType,
...
)
Arguments
poped.db |
A PopED database. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
maxxt |
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value. |
minxt |
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value |
maxa |
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value |
mina |
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
trflag |
Should the optimization be output to the screen and to a file? |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
cfaxt |
First step factor for sample times |
cfaa |
Stochastic Gradient search first step factor for covariates |
rsit |
Number of Random search iterations |
rsit_output |
Number of iterations in random search between screen output |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
ofv_calc_type |
OFV calculation type for FIM
|
approx_type |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI. |
bUseExchangeAlgorithm |
Use Exchange algorithm (1=TRUE, 0=FALSE) |
iter |
The number of iterations entered into the |
d_switch |
D-family design (1) or ED-family design (0) (with or without parameter uncertainty) |
ED_samp_size |
Sample size for E-family sampling |
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
... |
arguments passed to other functions. See |
Details
This function optimized the objective function. The function works for both discrete and continuous optimization variables. This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
References
M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
See Also
Other Optimize:
Doptim(),
LEDoptim(),
RS_opt(),
a_line_search(),
bfgsb_min(),
calc_autofocus(),
calc_ofv_and_grad(),
mfea(),
optim_ARS(),
optim_LS(),
poped_optim(),
poped_optim_1(),
poped_optim_2(),
poped_optim_3()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# D-family Optimization
##############
# below are a number of ways to optimize the problem
# RS+SG+LS optimization of DOSE and sample times
# optimization with just a few iterations
# only to check that things are working
out_1 <- poped_optimize(poped.db,opt_a=TRUE,opt_xt=TRUE,
rsit=2,sgit=2,ls_step_size=2,
iter_max=1,out_file = "")
## Not run:
# RS+SG+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optimize(poped.db,opt_xt=T)
get_rse(output$fmf,output$poped.db)
plot_model_prediction(output$poped.db)
# MFEA optimization with only integer times allowed
mfea.output <- poped_optimize(poped.db,opt_xt=1,
bUseExchangeAlgorithm=1,
EAStepSize=1)
get_rse(mfea.output$fmf,mfea.output$poped.db)
plot_model_prediction(mfea.output$poped.db)
# Examine efficiency of sampling windows
plot_efficiency_of_windows(mfea.output$poped.db,xt_windows=0.5)
plot_efficiency_of_windows(mfea.output$poped.db,xt_windows=1)
# Random search (just a few samples here)
rs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,rsit=20,
bUseRandomSearch= 1,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0)
get_rse(rs.output$fmf,rs.output$poped.db)
# line search, DOSE and sample time optimization
ls.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 1,
ls_step_size=10)
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 1,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0,
sgit=20)
# BFGS search, DOSE and sample time optimization
bfgs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 1,
bUseLineSearch = 0)
##############
# E-family Optimization
##############
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
# ED optimization using Random search (just a few samples here)
output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,rsit=10,d_switch=0)
get_rse(output$fmf,output$poped.db)
# ED with laplace approximation,
# optimization using Random search (just a few samples here)
output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,rsit=10,
d_switch=0,use_laplace=TRUE,laplace.fim=TRUE)
get_rse(output$fmf,output$poped.db)
## End(Not run)
Function written to match MATLAB's rand function
Description
Generate random samples from a uniform distribution [0,1] and return in matrix form
Usage
rand(dim1, dim2 = NULL)
Arguments
dim1 |
The dimension of the matrix (if square), otherwise the number of rows. |
dim2 |
The number of columns, if different from the number of rows. |
Value
Matrix of random generated samples.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
randn(),
size(),
tic(),
toc(),
zeros()
Examples
rand(2,3)
rand(5)
Function written to match MATLAB's randn function
Description
Generate random samples from a standardized normal distribution and return in matrix form.
Usage
randn(dim1, dim2 = NULL)
Arguments
dim1 |
The dimension of the matrix (if square), otherwise the number of rows. |
dim2 |
The number of columns, if different from the number of rows. |
Value
Matrix of random generated samples.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
size(),
tic(),
toc(),
zeros()
Examples
randn(2,3)
randn(5)
Predict shrinkage of empirical Bayes estimates (EBEs) in a population model
Description
Predict shrinkage of empirical Bayes estimates (EBEs) in a population model
Usage
shrinkage(poped.db, use_mc = FALSE, num_sim_ids = 1000, use_purrr = FALSE)
Arguments
poped.db |
A PopED database |
use_mc |
Should the calculation be based on monte-carlo simulations. If not then then a first order approximation is used |
num_sim_ids |
If |
use_purrr |
If |
Value
The shrinkage computed in variance units, standard deviation units and the relative standard errors of the EBEs.
References
Combes, F. P., Retout, S., Frey, N., & Mentre, F. (2013). Prediction of shrinkage of individual parameters using the Bayesian information matrix in non-linear mixed effect models with evaluation in pharmacokinetics. Pharmaceutical Research, 30(9), 2355-67. doi:10.1007/s11095-013-1079-3.
Hennig, S., Nyberg, J., Fanta, S., Backman, J. T., Hoppu, K., Hooker, A. C., & Karlsson, M. O. (2012). Application of the optimal design approach to improve a pretransplant drug dose finding design for ciclosporin. Journal of Clinical Pharmacology, 52(3), 347-360. doi:10.1177/0091270010397731.
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
shrinkage(poped.db)
Function written to match MATLAB's size function
Description
Function written to match MATLAB's size function
Usage
size(obj, dimension.index = NULL)
Arguments
obj |
An object you want to know the various dimensions of. Typically a matrix. |
dimension.index |
Which dimension you are interested in. |
Value
The dimensions of the object or specific dimension you are interested in.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
tic(),
toc(),
zeros()
Examples
size(c(2,3,4,5,6))
size(10)
size(zeros(4,7))
Start parallel computational processes
Description
This tool chooses the type of parallelization process to use based on the computer OS being used. For windows the default is "snow" and for Linux-like systems the default is "multicore"
Usage
start_parallel(
parallel = TRUE,
num_cores = NULL,
parallel_type = NULL,
seed = NULL,
dlls = NULL,
mrgsolve_model = NULL,
...
)
Arguments
parallel |
Should the parallel functionality start up? |
num_cores |
How many cores to use. Default is
|
parallel_type |
Which type of parallelization should be used? Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on Linux-like systems. By default this is chosen for you depending on your operating system. |
seed |
The random seed to use. |
dlls |
If the computations require compiled code (DLL's) and you are
using the "snow" method then you need to specify the name of the DLL's without
the extension as a text vector |
mrgsolve_model |
If the computations require a mrgsolve model and you
are using the "snow" method" then you need to specify the name of the model
object created by |
... |
Arguments passed to |
Value
An atomic vector (TRUE or FALSE) with two attributes: "type" and "cores".
Display a summary of output from poped_optim
Description
Display a summary of output from poped_optim
Usage
## S3 method for class 'poped_optim'
summary(object, ...)
Arguments
object |
An object returned from |
... |
Additional arguments. Passed to |
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# D-family Optimization
##############
# ARS+BFGS+LS optimization of dose
# optimization with just a few iterations
# only to check that things are working
out_1 <- poped_optim(poped.db,opt_a =TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
summary(out_1)
Test to make sure that matricies are the right size
Description
Test to make sure that matricies are the right size
Usage
test_mat_size(correct_size, mat, name)
Arguments
correct_size |
the correct size of a matrix |
mat |
The matrix to test. |
name |
The name of the matrix as a string. |
Examples
test_mat_size(c(2,3),zeros(2,3),"foo")
## Not run:
test_mat_size(c(2,3),zeros(2,6),"foo")
## End(Not run)
test_mat_size(c(1,3),c(2,6,7),"foo")
Timer function (as in MATLAB)
Description
Function to start a timer. Stop with toc().
Usage
tic(gcFirst = FALSE, name = ".poped_savedTime")
Arguments
gcFirst |
Perform garbage collection? |
name |
The saved name of the time object. |
Note
This is a modified version of the same function in the matlab R-package.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
size(),
toc(),
zeros()
Examples
tic()
toc()
tic(name="foo")
toc()
tic()
toc()
toc()
tic()
toc(name="foo")
Timer function (as in MATLAB)
Description
Function to stop a timer. Start with tic().
Usage
toc(echo = TRUE, name = ".poped_savedTime")
Arguments
echo |
Print time to screen? |
name |
The saved name of the time object. |
Note
This is a modified version of the same function in the matlab R-package.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
size(),
tic(),
zeros()
Examples
tic()
toc()
tic(name="foo")
toc()
tic()
toc()
toc()
tic()
toc(name="foo")
tryCatch both warnings (with value) and errors
Description
Catch *and* save both errors and warnings, and in the case of a warning, also keep the computed result.
Usage
tryCatch.W.E(expr)
Arguments
expr |
an R expression to evaluate |
Value
a list with 'value' and 'warning', where 'value' may be an error caught.
Author(s)
Martin Maechler, The R Core Team
Create a matrix of zeros.
Description
Create a matrix of zeros of size (dim1 x dim2).
Usage
zeros(dim1, dim2 = NULL)
Arguments
dim1 |
The dimension of the matrix (if square) or the number of rows. |
dim2 |
The number of columns |
Value
A matrix of zeros.
See Also
Other MATLAB:
cell(),
diag_matlab(),
feval(),
fileparts(),
isempty(),
ones(),
rand(),
randn(),
size(),
tic(),
toc()
Examples
zeros(3)
zeros(0,3)
zeros(4,7)
zeros(1,4)