Calculate a weighted average of pglm

Description

These function takes the output of pgls.iter and uses its list of objects model fits, optimzations (e.g.AICc) and performs a weighted average on the ctoefficients estimated in the former by weighting by the latter. The parameters can also optionally be converted to binary by specifying "binary=FALSE" or just running the alias wraper function for assessing evidence of variable importance (Burnham & Anderson 2000).

Usage

average.fit.models(vars, fits, optims,weight='AICw',
         by=c('n','q','nXq','rwGsm')[1], round.digits=5, binary=FALSE, standardize=FALSE)
variable.importance(vars, fits, optims,weight='AICw',
         by=c('n','q','nXq','rwGsm')[1], round.digits=5)

Arguments

Value

A vector of AICc difference weighted [AICw] averages of PGLS coefficients. Also returns model 'selection' errors or the square root of 'uncertainties' (Burnham & Anderson 2000)

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

average.fit.models(vars=c('mass.Kg','group.size'), fits=PGLSi$fits, optims=PGLSi$optim)
variable.importance(vars=c('mass.Kg','group.size'), fits=PGLSi$fits, optims=PGLSi$optim)

Calculate the ratio of fit predictor variables to sample size

Description

The one in ten rule of thumb for model fitting suggest at least 10 fold as many data as parametes fit. This function allows for easily calculating that ratio on model selected PGLS fits.

Usage

calc.q2n.ratio(coefs)

Arguments

Value

the ratio of q to n (on average for all extracted fit models)

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

coefs.objs <- get.pgls.coefs(PGLSi$fits, est='Estimate')

calc.q2n.ratio(coefs.objs)

Include all variables except ...

Description

This function takes a dataframe, list, or a named vector of variable (column) names to subset

Usage

cept(x,except='gn_sp')

Arguments

Value

the subset of x without those 'except' items specified

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)

df.except.gnsp <- cept(x=data,except='gn_sp')

Comparative Data

Description

This is a shortcut function that wraps around "comparative.data" for use in the PGLS function.

Usage

comp.data(phylo,df,gn_sp='gn_sp')

Arguments

Value

a "comparative data" table for use in PGLS modeling

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

Find data being dropped by mismatches to the tree

Description

This function simply lists the rows of the data that are not getting matched to tips of the tree.

Usage

compare.data.gs.vs.tree.tips(data, phylo, match.on=c('gn_sp','rownames')[1])

Arguments

Value

prints rows that are not matched ot the tree tips

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]


compare.data.gs.vs.tree.tips(data, phyl, match.on='rownames')

Correct AIC

Description

Calculate a corrected Akaiki Information Criterion

Usage

correct.AIC(AIC, K,n)

Arguments

Value

corrected AIC values

Examples

correct.AIC(AIC=100,K=10,n=100)

Count the predictor variables in a model

Description

This function takes a model string and counts the number of predictor variables.

Usage

count.mod.vars(model)

Arguments

Value

an integer specifying the count of predictor variables

Examples

count <- count.mod.vars(model=formula('y ~ x1 + x2'))
if(count == 2) { print('sane'); }else{ print('insane')} 

Count all possible model combinations

Description

Count all combinations of predictor variables in a multivariate regression model.

Usage

ct.possible.models(q)

Arguments

Value

a count of the number of possible models

Examples

ct.possible.models(9)

Drop any rows with NA values

Description

This function takes a dataframe as input and removes any rows that have NA as values.

Usage

drop.na.data(df, vars=names(df))

Arguments

Value

A subset of 'df' that only has non-missing values in the columns specified by 'vars'

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path, row.names=1)

df.nona <- drop.na.data(data, vars=names(df))

Report a model fit in a single line of text output

Description

This function takes a fit multivariate regression model as input and converts the normal tabular output into a single line using repeated "+"or"-" symbols for significance

Usage

fit.1ln.rprt(fit, method=c('std.dev','p-value')[1], decimal.places=3, 
             name.char.len=6, print.inline=TRUE, rtrn.line=FALSE, R2AIC=TRUE,mn='')

Arguments

Value

A character string of the form "++var1 +var5 var3 | -var2 –var4" indicating signifcance and direction of regression results

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path, row.names=1)

model.fit <- lm('OC ~ mass.Kg + group.size + arboreal + leap.pct', data=data)

fit.1ln.rprt(fit=model.fit, decimal.places=3, name.char.len=6, print.inline=TRUE, rtrn.line=FALSE)

Get model columns

Description

Get the variable names from a model string by splitting on "+" and '~' using both 'get.mod.outcome' and 'get.mod.vars'. The results are passed to the comp.data function for eventual use in PGLS modeling. 'gn_sp' is included as it is typically required to link tree tips to rows of the comparative data.

Usage

get.mod.clmns(model, gs.clmn='gn_sp')

Arguments

Value

a vector of characters enummerating the columns to retain in PGLS modeling (input to df param in the 'comp.data' function)

Examples

model.columns <- get.mod.clmns(model=formula('y ~ x1 + x2'))

Get the outcome variable from a model string

Description

Get the outcome variable from the front of a model formula string. Used as part of 'get.mod.clmns' to be passed to 'comp.data'

Usage

get.mod.outcome(model)

Arguments

Value

a character string specifying the outcome variable

Examples

model.columns <- get.mod.clmns(model=formula('y ~ x1 + x2'))

Get model variable names

Description

Split the predictor string of a model formula into it's constituent character strings.

Usage

get.mod.vars(model)

Arguments

Value

a vector of character strings of variable names (e.g. corresponding to column names for comp.data input)

Examples

model.variables <- get.mod.vars(model='y ~ x1 + x2')

All combinations of predictor variables

Description

Enumerate all combinations of predictor variables in a multivariate regression model.

Usage

get.model.combos(outcome.var, predictor.vars, min.q=1)

Arguments

Value

a vector of models as character strings of the form "y ~ x1 + x2 ..."

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path, row.names=1)

get.model.combos(outcome.var='OC', predictor.vars=names(data),  min.q=2)

Get coeficients from a list of PGLS model-fits (from each selected subset)

Description

Post PGLS model selection, the list of all possible PGLS model fits can be subset and passed to this function, which harvests out the coefficients or t-values for each model into bins for the coefficients

Usage

get.pgls.coefs(pgls.fits, est=c("t value","Estimate","Pr(>|t|)")[1])

Arguments

Value

A list of PGLS coeficients (lists of estimates and t-values) organized by coeficient-named bins

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

coefs.objs <- get.pgls.coefs(PGLSi$fits, est='Estimate')

Get tree statistics for a trait

Description

This function uses Pagel's lambda, Blombergs k, and Ancestral Character Estimation [ACE] to calculate statistics on a tree given a specified trait.

Usage

get.phylo.stats(phylo, data, trait.clmn, gs.clmn='gn_sp', 
                ace.method='REML',ace.scaled=TRUE, ace.kappa=1)

Arguments

Value

statistics on a particular trait within a tree (Pagel's lambda, Blomberg's k, and the most ancestral ACE estimate)

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)

data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

get.phylo.stats(phylo=phyl, data=data, trait.clmn='OC', 
        gs.clmn='gn_sp', ace.method='REML',ace.scaled=TRUE, ace.kappa=1)

Check "Genus species" name formatting

Description

This convienience function checks to make sure that all of the elements the provided character vector adhere to the "Genus species" naming convention format. Default delimiters between genus and species names in the string are " ", "_", or "."

Usage

gs.check(genus.species, sep='[ _\\.]')

Arguments

Value

None

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
gs.tab <- read.csv(path, row.names=1)
gs.tab$gn_sp <- rownames(gs.tab)

gs.check(genus.species=gs.tab$gn_sp, sep='[ _\\.]')

Check "Genus species" name formatting

Description

This convienience function checks to make sure that all of the elements the provided character vector adhere to the "Genus species" naming convention format. Default delimiters between genus and species names in the string are " ", "_", or "."

Usage

gs.names.mismatch.check(df, alias.table.path, gs.clmn='gn_sp')

Arguments

Value

None

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
gs.tab <- read.csv(path, row.names=1)
gs.tab$gn_sp <- rownames(gs.tab)

path.look <- system.file("extdata","primate.taxa.aliases.tab", package="mmodely")

gs.names.mismatch.check(gs.tab, alias.table.path=path.look, gs.clmn='gn_sp')

Rename the Genus species information in a data frame

Description

This function takes a data frame (with a genus species column) and proceeds to use an external look-up table to update the names if they've been changed

Usage

gs.rename(df, alias.table.path, retro=FALSE, update.gn_sp=FALSE)

Arguments

Value

the original data frame with (potentially) updated row names and updated gn_sp column values

Examples

path.data <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path.data, row.names=1)

path.look <- system.file("extdata","primate.taxa.aliases.tab", package="mmodely")

data.renamed <- gs.rename(df=data, alias.table.path=path.look, retro=FALSE, update.gn_sp=FALSE)

Interpolate missing data in a data frame

Description

This function finds NA values and interpolates using averaging values of nearby genus and species

Usage

interpolate(df, taxa=c('genus','family'), clmns=1:length(df))

Arguments

Value

a modified data frame without missing values in the columns specified

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
gs.tab <- read.csv(path, row.names=1)

clmns <- match(c('mass.Kg','DPL.km'),names(gs.tab))
df.2 <- interpolate(df=gs.tab, taxa='genus', clmns=clmns)

Report missing values in a dataframe

Description

This funciton reports column and rowwise missing data. It can also list the rownames for missing columns or the column names for missing rows.

Usage

missing.data(x, cols=NULL, rows=NULL)

Arguments

Value

a report on column versus row wise missing data

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path, row.names=1)

missing.data(data)

Fill in missing values in a dataframe with a secondary source

Description

This function uses the (non-missing) values from one column to fill in the missing values of another

Usage

missing.fill.in(x, var.from, var.to)

Arguments

Value

a modified dataframe with fewer missing values in the 'var.to' column

Examples

df <- data.frame(a=c(1,2,NA),b=c(1,NA,3),c=c(1,2,6))
missing.fill.in(df, 'c','a')

Iterate through PGLS estimations

Description

This function takes phylogenetic tree and a list of (all possible) combinations of variables as a vector of model strings and estimates PGLS fits based on the bounds or tree parameters provided seperately.

Usage

pgls.iter(models, phylo, df,  gs.clmn='gn_sp', 
          b=list(lambda=c(.2,1),kappa=c(.2,2.8),delta=c(.2,2.8)),l='ML', k='ML',d='ML') 

Arguments

Value

a list of fit PGLS regression models plus 'optim' and 'param' support tables

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

Statistics from PGLS runs

Description

Print (and plot) statistics from a list of PGLSs fitted models and tables of associated parameters.

Usage

pgls.iter.stats(PGLSi, verbose=TRUE, plots=FALSE)

Arguments

Value

A summary statistics on each of the objects in the PGLS list of lists

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

pgls.iter.stats(PGLSi, verbose=TRUE, plots=FALSE)

Print the results of a PGLS model fit

Description

Print the results of a PGLS model fit

Usage

pgls.print(pgls, all.vars=names(pgls$data$data)[-1], 
           model.no=NA, mtx.out=NA, write=TRUE, print=FALSE)

Arguments

Value

A matrix of summary results of a fit PGLS model

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")

#5. RAxML phylogram based on the 61199 bp concatenation of 69 nuclear and ten mitochondrial genes.
phyl <- ape::read.tree(tree.path)[[5]]

phyl <- trim.phylo(phylo=phyl, gs.vect=data$gn_sp)

comp <- comp.data(phylo=phyl, df=data)

a.PGLS <- caper::pgls(formula('OC~mass.Kg + DPL.km'),  data=comp)

pgls.print(a.PGLS, all.vars=names(a.PGLS$data$data)[-1],
           model.no=NA, mtx.out='', write=FALSE, print=FALSE)

Report PGLS results as a table

Description

Output a spreadsheet ready tabular summary of a fit PGLS model

Usage

pgls.report(cd, f=formula('y~x'), l=1,k=1,d=1, 
            bounds=list(lambda=c(.2,1),kappa=c(.2,2.7),delta=c(.2,2.7)), 
            anova=FALSE, mod.no='NA', out='pgls.output-temp',QC.plot=FALSE)

Arguments

Value

A summary results of a fit PGLS model with ANOVA and tabular spreadsheet ready csv filesystem output.

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
#5. RAxML phylogram based on the 61199 bp concatenation of 69 nuclear and ten mitochondrial genes. 
phyl <- ape::read.tree(tree.path)[[5]]

phyl <- trim.phylo(phylo=phyl, gs.vect=data$gn_sp)

comp <- comp.data(phylo=phyl, df=data)

pgls.report(comp, f=formula('OC~mass.Kg + DPL.km'), l=1,k=1,d=1,
             anova=FALSE, mod.no='555', out='', QC.plot=TRUE)

A Wrapper for PGLS model

Description

Print the results of an unfit PGLS model

Usage

pgls.wrap(cd,f,b,l,k,d,all.vars=names(cd$data)[-1], 
           model.no=NA, mtx.out=NA, write=TRUE,print=FALSE)

Arguments

Value

A matrix of summary results of a fit PGLS model

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
#5. RAxML phylogram based on the 61199 bp concatenation of 69 nuclear and ten mitochondrial genes.
phyl <- ape::read.tree(tree.path)[[5]]

phyl <- trim.phylo(phylo=phyl, gs.vect=data$gn_sp)

comp <- comp.data(phylo=phyl, df=data)

model <- 'OC ~ mass.Kg + group.size'

pgls.wrap(cd=comp,f=model,b=list(kappa=c(.3,3),lambda=c(.3,3),delta=c(.3,3)), 
           l=1,k=1,d=1,all.vars=names(cd.obj$data)[-1])

Plot a grid of x y plots split by a confounder z

Description

Plot a grid of x y plots showing how a third confounding variable 'z' changes the slope

Usage

## S3 method for class 'confound.grid'
plot(x, Y='y', X='x', confounder='z', breaks=3,...)

Arguments

Value

a confound grid plot

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path, row.names=1)
data$col <- c('yellow','red')[data$nocturnal+1]

plot.confound.grid(x=data, Y='OC', X='leap.pct', confounder='mass.Kg')

Plot (R2 vs AIC) results of a collection of fit PGLS models

Description

Plots a single panel of R^2 versus AIC, using versions of your choosing.

Usage

## S3 method for class 'pgls.R2AIC'
plot(x, 
            bests=bestBy(x, by=c('n','q','qXn','rwGsm')[4], best=c('AICc','R2.adj')[1], 
            inverse=c(FALSE,TRUE)[1]),bcl=rgb(1,1,1,maxColorValue=3,alpha=1), nx=2, 
            model.as.title='', ...)

Arguments

Value

a plot of R2 versus AIC of many PGLS models

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:6])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

# sprinkle in some missing data so as to make model selection more interesting
for(pv in pvs){ data[sample(x=1:nrow(data),size=2),pv] <- NA} 

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

plot.pgls.R2AIC(PGLSi$optim)   # find the lowest AIC within each q by n sized sub-dataset

Plot the PGLS iterations

Description

A plot of AIC (and AICc) vs R^2 (and adjusted R^2) for all of the PGLS iterations

Usage

## S3 method for class 'pgls.iters'
plot(x, 
      bests=bestBy(x$optim, by=c('n','q','qXn','rwGsm')[1], best=c('AICc','R2.adj')[1], 
                         inverse=FALSE), ...)

Arguments

Value

a plot of all of PGLS iterations

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 


# sprinkle in some missing data so as to make model selection more interesting
for(pv in pvs){ data[sample(x=1:nrow(data),size=2),pv] <- NA} 

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

# find the lowest AIC within each q by n sized sub-dataset
plot.pgls.iters(x=PGLSi)

Plot a transformed phylogenetic tree

Description

PGLS regression will use maximum likelihood to estimate tree parameters while also estimating regression parameters. Here we provide a utility function to visualize what this new tree would look like in two dimensions.

Usage

## S3 method for class 'transformed.phylo'
plot(x, delta=1,kappa=1,...)

Arguments

Value

a plot of a transformed phylogenetic tree

Examples

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

plot.transformed.phylo(x=phyl, delta=2.3,kappa=2.1)

An x/y scatterplot with a linear regression line and p-value

Description

This function performs a simple scatter plot but also superimposses a linear regression trend (abline) and optionally also the p-value of this line

Usage

## S3 method for class 'xy.ab.p'
plot(x, x.var, y.var, 
fit.line=TRUE, p.value=TRUE, slope=TRUE, p.col='red', plot.labels=TRUE, verbose=TRUE, ...)

Arguments

Value

An x/y scatterplot with regression line

Examples

path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(path, row.names=1)

plot.xy.ab.p(x=data, x.var='OC', y.var='group.size', 
        fit.line=TRUE, p.value=TRUE, slope=TRUE, p.col='red', plot.labels=TRUE, verbose=TRUE)

Get the best model from list of PGLS model fits

Description

Get the outcome variable from the front of a model formula string. Used as part of 'get.mod.clmns' to be passed to 'comp.data'

Usage

select.best.models(PGLSi, using=c('AICc','R2.adj','AIC','R2')[1],
                             by=c('n','q','nXq','rwGsm')[1])

Arguments

Value

a line corresponding to the "best" models from the PGLSi "optim" table

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

a.PGLS <- select.best.models(PGLSi, by=c('R2.adj','AICc')[1])

Coeficients distribution [sparge] plot of models selected from each subset

Description

Plot the raw distribution of points corresponding to the coefficients harvested from the best model of each subset of the dataset.

Usage

sparge.modsel(PC, jit.f=1, R2x=3, nx=2, n.max=max(unlist(PC$n)), zeroline=TRUE,
                  add=FALSE, pd=0, pvs=names(PC$coefs), pvlabs=NULL, 
                  xlim=range(unlist(PC$coefs)),
                  MA = NULL, ap=8, ac = 1, ax = nx, ...)

Arguments

Value

a 'sparge' [sprinkle/smear] plot of coefficent distributions

See Also

See also 'boxplot' and 'stripchart' in package 'graphics' as well as 'violin', 'bean', 'ridgelines', and 'raincloud' plots.

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

coefs.objs <- get.pgls.coefs(PGLSi$fits, est='Estimate')

sparge.modsel(coefs.objs)

Trim a phylogenetic tree using Genus species names

Description

Read in a vector of genus species names and a tree and drop the tips in the tree that match the vector of names.

Usage

trim.phylo(phylo, gs.vect)

Arguments

Value

a plot of a transformed phylogenetic tree

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phylo <- read.tree(tree.path)[[5]]

trim.phylo(phylo, gs.vect=data$gn_sp)

Get IC weights

Description

An implementation of IC weighting that first calulates the difference in IC values by subtracting all values from the lowest IC value. Second, the changes are expoentiated divided by a sum of the same and exponentiated yet again.

Usage

weight.IC(IC)

Arguments

Value

a vector of IC based weights

Examples

data.path <- system.file("extdata","primate-example.data.csv", package="mmodely")
data <- read.csv(data.path, row.names=1)
pvs <- names(data[3:5])
data$gn_sp <- rownames(data)

tree.path <- system.file("extdata","primate-springer.2012.tre", package="mmodely")
phyl <- ape::read.tree(tree.path)[[5]]

comp <- comp.data(phylo=phyl, df=data)

mods <- get.model.combos(predictor.vars=pvs, outcome.var='OC', min.q=2)

PGLSi <- pgls.iter(models=mods, phylo=phyl, df=data, k=1,l=1,d=1) 

AICc.w <- weight.IC(IC=PGLSi$optim$AICc)