sparge.modsel {mmodely}

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

PC

a list of vectors of pooled coefficients (or scores) harvested from the 'best' selected modeling runs (out put from 'get.pgls.coefs')

jit.f

factor for random jittering (see 'jitter()'

R2x

the line width expansion factor according to R^2 value

nx

the point size expansion factor according to sample size of model

n.max

the maximum sample size used in all models

zeroline

should we add an abline at x=0?

add

should we add to the existing plot?

pd

'position dodge' moves all y axis plotting positions up or down by this provided value (useful for adding multiple distributions for the same param)

pvs

the predictor variable vector for ordering the y-axis labels

pvlabs

the predictor variable labels for labeling the plot (defaults to pvs)

xlim

x axis plot limits

MA

matrix of model averages (defaults to NULL)

ap

coded numeric point character symbol used for model averaged parameter position

ac

color symbol used for model averaged parameters plot character

ax

expansion factor to expant model average parameter plot character (defaults to nx)

...

other parameters passed on to plot

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)