geomorph (version 3.1.2)

physignal: Assessing phylogenetic signal in Procrustes shape variables

Description

Function calculates the degree of phylogenetic signal from Procrustes shape variables

Usage

physignal(A, phy, iter = 999, seed = NULL, print.progress = TRUE)

Arguments

A

A matrix (n x [p x k]) or 3D array (p x k x n) containing Procrustes shape variables for a set of specimens

phy

A phylogenetic tree of class phylo - see read.tree in library ape

iter

Number of iterations for significance testing

seed

An optional argument for setting the seed for random permutations of the resampling procedure. If left NULL (the default), the exact same P-values will be found for repeated runs of the analysis (with the same number of iterations). If seed = "random", a random seed will be used, and P-values will vary. One can also specify an integer for specific seed values, which might be of interest for advanced users.

print.progress

A logical value to indicate whether a progress bar should be printed to the screen. This is helpful for long-running analyses.

Value

Function returns a list with the following components:

phy.signal

The estimate of phylogenetic signal

pvalue

The significance level of the observed signal

random.K

Each random K-statistic from random permutations

permutations

The number of random permutations used in the resampling procedure

call

The matched call

Details

The function estimates the degree of phylogenetic signal present in Procrustes shape variables for a given phylogeny. It is assumed that the landmarks have previously been aligned using Generalized Procrustes Analysis (GPA) [e.g., with gpagen]. The degree of phylogenetic signal in data is estimated using the multivariate version of the K-statistic (Kmult: Adams 2014). This value evaluates the degree of phylogenetic signal in a dataset relative to what is expected under a Brownian motion model of evolution. For geometric morphometric data, the approach is a mathematical generalization of the Kappa statistic (Blomberg et al. 2003) appropriate for highly multivariate data (see Adams 2014).Significance testing is found by permuting the shape data among the tips of the phylogeny. Note that this method can be quite slow as ancestral states must be estimated for every iteration.

This function can also be used with univariate data (i.e. centroid size) if imported as matrix with rownames giving the taxa names. In this case, the estimate of phylogenetic signal is identical to that found using the standard kappa statistic (Blomberg et al. 2003).

The generic functions, print, summary, and plot all work with physignal. The generic function, plot, produces a histogram of random K statistics, associated with the resampling procedure.

Notes for geomorph 3.0

Compared to older versions of geomorph, the order of input variables has changed, so that it is consistent with other functions in the program. Additionally, users should note that the function physignal no longer contains multiple methods. Only Kmult is used. Thus, for older scripts method="" should be removed from the function call.

References

Blomberg SP, Garland T, Ives AR. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution, 57:717-745.

Adams, D.C. 2014. A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data. Systematic Biology. 63:685-697.

Examples

# NOT RUN {
data(plethspecies) 
Y.gpa<-gpagen(plethspecies$land)    #GPA-alignment    

#Test for phylogenetic signal in shape
PS.shape <- physignal(A=Y.gpa$coords,phy=plethspecies$phy,iter=999)
summary(PS.shape)
plot(PS.shape)

#Test for phylogenetic signal in size
PS.size <- physignal(A=Y.gpa$Csize,phy=plethspecies$phy,iter=999)
summary(PS.size)
plot(PS.size)
# }