phylo.integration(A, A2 = NULL, phy, partition.gp = NULL, iter = 999, seed = NULL, print.progress = TRUE)read.tree in library apegpagen].The function estimates the degree of morphological covariation between two or sets of variables while accounting for phylogeny using partial least squares (Adams and Felice 2014), and under a Brownian motion model of evolution. If more than two partitions are defined, the average pairwise PLS correlation is utilized as the test statistic. The observed value is statistically assessed using permutation, where data for one partition are permuted relative to the other partitions. Note that this permutation is performed on phylogenetically- transformed data, so that the probability of phylogenetic association of A vs. B is similar to that of B vs. A: i.e., prob(A,B|phy)~prob(B,A|phy).
Input for the analysis can take one of two forms. First, one can input a single dataset (as a matrix or 3D array, along with a vector describing which variables correspond to which partitions (for the case of a 3D array, which landmarks belong to which partitions is specified). Alternatively, when evaluating the integration between two structures or partitions, two datasets may be provided.
The generic functions, print, summary, and plot all work with phylo.integration.
The generic function, plot, produces a two-block.pls plot. This function calls plot.pls, which has two additional
arguments (with defaults): label = NULL, warpgrids = TRUE. These arguments allow one to include a vector to label points and a logical statement to
include warpgrids, respectively. Warpgrids can only be included for 3D arrays of Procrustes residuals. The plot is a plot of PLS scores from
Block1 versus Block2 performed for the first set of PLS axes.
integration.test, modularity.test, phylo.pls, and
two.b.pls
data(plethspecies)
Y.gpa<-gpagen(plethspecies$land) #GPA-alignment
land.gps<-c("A","A","A","A","A","B","B","B","B","B","B")
IT<- phylo.integration(Y.gpa$coords,partition.gp=land.gps,phy=plethspecies$phy,iter=999)
summary(IT) # Test summary
plot(IT) # PLS plot
Run the code above in your browser using DataLab