PVR.adonis: Phylogenetic Eigenvector Regression (PVR) and eigenvector...

In vanderleidebastiani/daee: Data Analysis for Ecology and Evolution

Description

Phylogenetic Eigenvector Regression (PVR) and eigenvector selection using analysis of variance with distance matrices (adonis).

Usage

PVR.adonis(traits, dist, cumulative = 0.99)

Arguments

traits	Data matrix or a dissimilarity matrix (recommended), usually related to species traits. This will be passed to the left side of the formula in the adonis function. The sequence of species in the traits data matrix or dissimilarity matrix must be the same as that in the phylogenetic distance matrix. See details in adonis.
dist	Phylogenetic distance matrix.
cumulative	Percentage of variation in the phylogenetic distances considered in the analysis. Cumulative percentage must be higher than the cumulative percentage of the first two eigenvalues, and less than 1.

Details

The phylogenetic distance matrix is double-centered and submitted to principal coordinates analysis (PCoA). This method generates orthogonal eigenvectors that summarize the phylogenetic structure (Diniz-Filho et al 2008).

This function is similar the function PVR that use a non-sequential approach to perform the eigenvector selection, but the selection is based in multivariate analysis of variance. The function search to combination of eigenvectors that maximize the F value in the analysis of variance with distance matrices using the adonis function. Primarily, an analysis for each eigenvectors is performed, obtaining the F values. Then, the function select the eingenvector with the higher F value, and then, new eigenvectors are added to the model, models are updated and F values are obtained. The search stops when all eigenvectors are included in the model. The subset of eigenvectors that maximize the global F value must be selected manually in the results.

Value

values	Eigenvalues, relative eigenvalues and cumulative eigenvalues for the PCoA of distance matrix.
vectors	The principal coordinates with positive eigenvalues.
inf.cumulative	Percentage of the variation in the phylogenetic distances considered in the analysis (The result should be approximately the specified cumulative value).
n.axis.considered	Number of axes considered.
results.unique	F value for each PVR axis
results.sequential	F value for sequential approach using all PVR axes (PVR 1,PVR 1 + PVR 2, ...).
results.stepwise	F value for non-sequential approach, that uses the combination of PVRs axes that maximize the F value. The selection finishes using all PVRs considered, but the max F value must be selected manually in the results.

Author(s)

Vanderlei Julio Debastiani <[e-mail]>

References

Diniz-Filho, J. A. F., Santana, C. E. R., Bini, L. M. (1998). An eigenvector method for estimating phylogenetic inertia. Evolution, 52(5), 1247-1262.

See Also

PVR

Examples

# require(SYNCSA)
# require(vegan)
# data(flona)
# traits.dist <- vegdist(decostand(flona$traits[,c(1,3)], 
# 								 method = "standardize"), 
# 					   method = "euclidean")
# results <- PVR.adonis(traits.dist, flona$phylo, cumulative = 0.7)
# results
# plot(factor(results$results.unique$PVR, levels =results$results.unique$PVR), 
# 	 results$results.unique$F.value, 
# 	 xlab = "PVR", ylab = "F value", main = "results.unique")
# plot(factor(results$results.sequential$PVRs, levels = results$results.sequential$PVRs), 
# 	 results$results.sequential$F.value,
# 	 xlab = "PVRs", ylab = "F value", main = "results.sequential")
# plot(factor(results$results.stepwise$PVRs, levels = results$results.stepwise$PVRs), 
# 	 results$results.stepwise$F.value,
# 	 xlab = "PVRs", ylab = "F value", main = "results.stepwise")

vanderleidebastiani/daee documentation built on Jan. 22, 2021, 2:41 p.m.