plot.pgls.iters: Plot the PGLS iterations

In mmodely: Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology

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

x	a PGLSi[teration] object (a list of pgls model fits as well as optimization and tree parameter tables)
bests	a table of the 'best' models to highlight in the plot based on some optimization criterion (e.g. R2)
...	other parameters passed to 'plot'

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)

mmodely documentation built on May 31, 2023, 6:47 p.m.