geomorph (version 3.0.3)

compare.pls: Comparisons of Effect Sizes from Partial Least Squares

Description

Function performs an analysis to compare the effect sizes of two or more PLS effects

Usage

compare.pls(...)

Arguments

...

saved analyses of class, "pls"

Value

An object of class, "compare.pls", returns a list of the following

sample.z

A vector of effect sizes for each sample.

sample.r.sd

A vector of standard deviations for each sampling distribution.

pairwise.z

A matrix of pariwise, two-sample z scores betwen all pairs of effect sizes.

pairwise.p

A matrix of corresponding P-values.

Details

The function statistically compares the effect sizes of two or more PLS analyses. Typically, this function might be used to compare levels of integration between two or more samples, each measuring morphological integration between different modules. In such cases, the PLS correlation coefficient, r, is not a good measure of integration effect, as its expected value is dependent on both the number of specimens and number of variables (Adams and Collyer 2016). This analysis calculates effect sizes as standard deviates, z, and performs two-sample z-tests, using the pooled standard error from the sampling distributions of the PLS analyses.

To use this function, simply perform two.b.pls, integration.test, or phylo.integration on as many samples as desired. Any number of objects of class "pls" can be input.

Similar versions of this function will designed for alternative test statistics, in the future.

References

Adams, D.C and M.L. Collyer. 2016. On the comparison of the strength of morphological integration across morphometric datasets. Evolution (in press)

Examples

# Example of comparative morphological intgration between pupfish head and body shapes

 data(pupfish) # GPA previously performed
  
 group <- factor(paste(pupfish$Pop, pupfish$Sex, sep = "."))
 levels(group)
  
 tail.LM <- c(1:3, 5:9, 18:38)
 head.LM <- (1:56)[-tail.LM]

 tail.coords <- pupfish$coords[tail.LM,,]
 head.coords <- pupfish$coords[head.LM,,]

 # Subset by group
 tail.coords.gp <- coords.subset(tail.coords, group)
 head.coords.gp <- coords.subset(head.coords, group)

 integ.tests <- Map(function(x,y) integration.test(x, y, iter=499), head.coords.gp, tail.coords.gp)

 integ.tests$Marsh.F
 integ.tests$Marsh.M
 integ.tests$Sinkhole.F
 integ.tests$Sinkhole.M

 group.Z <- compare.pls(integ.tests)
 summary(group.Z)

 # Sexual dimorphism in morpholigical intrgartion in one population
 # but not the other

 # can also list different PLS analyses, seprately

 compare.pls(MF = integ.tests$Marsh.F, MM = integ.tests$Marsh.M)