| Title: | Integration-Based Coefficients of Variance |
| Version: | 0.0.1.0 |
| Description: | Estimate coefficient of variance percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2024-03-26 12:47:11 UTC; PRYBYJ |
| Author: | John Prybylski 
[aut, cre] |
| Maintainer: | John Prybylski <john.prybylski@pfizer.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-03-26 17:00:09 UTC |
Built-in integration-based %CV functions
Description
Built-in integration-based %CV functions
Usage
dist.intcv(
dist = "log",
...,
exact = ifelse(dist == "log", TRUE, FALSE),
lambda = NULL,
fun = FALSE
)
Arguments
dist |
Selection of built-in distributions. |
... |
passed to moment() |
exact |
If there is an exact moment generating function, use that. Default TRUE only for log |
lambda |
shape parameter for nonmemboxcox() |
fun |
return function (for use in invcv()) |
Value
Percent CV
Built-in moment functions
Description
Built-in moment functions
Usage
dist.moment(
dist = "log",
...,
exact = ifelse(dist == "log", TRUE, FALSE),
lambda = NULL
)
Arguments
dist |
Selection of built-in distributions. |
... |
passed to moment() |
exact |
If there is an exact moment generating function, use that. Default TRUE only for log |
lambda |
shape parameter for nonmemboxcox() |
Value
moment
Integration-based CV%
Description
Integration-based CV%
Usage
intcv(...)
Arguments
... |
Arguments passed to moment() |
Value
Percent CV
Variance from CV%
Description
Variance from CV%
Usage
invcv(cvfun, cv, verbose = FALSE, ...)
Arguments
cvfun |
intcv()-based function |
cv |
CV% generated from cvfun |
verbose |
extra output |
... |
Other parameters to pass to cvfun |
Value
Best-fit variance
Moment function
Description
Moment function
Usage
moment(...)
Arguments
... |
all arguments passed to moment_f() |
Value
moment
Integratable moment function
Description
Integratable moment function
Usage
moment_f(x, u, v, n, pdist, qdist)
Arguments
x |
numeric vector |
u |
mean |
v |
variance |
n |
moment number |
pdist |
un-transform function for transformed random variable (eg, exp()) |
qdist |
transform function (eg, log()) |
Value
Point result of the moment function
Box-Cox transform typically used in NONMEM
Description
Parameters are typically treated as lognormally-distributed by NONMEM users. Box-Cox transforms are typically applied to the exponentiated individual ETA parameters; this means the parameter is neither Box-Cox distributed nor lognormally-distributed, but both. To get the "Box-Cox Transform" as it would be relevant for CV% calculation, these properties have to be considered.
Usage
nonmemboxcox(x, lambda, theta = 1, inv = FALSE)
Arguments
x |
random vector. Must be positive. |
lambda |
shape parameter |
theta |
centrality parameter |
inv |
inverse transform |
Value
Box-Cox transformed or untransformed vector
Numeric CV% of a sample
Description
Numeric CV% of a sample
Usage
numcv(x, ...)
Arguments
x |
numeric vector |
... |
other arguments for sd() and mean() |
Value
Percent cv
Examples
test_x <- rnorm(1000, mean=50, sd=5)
cv <- numcv(test_x)
cv # expect ~ 10(%)