Fast covariance-based implementations of distance-based methods

Description

The functions fast.geomorph.compare.evol.rates, fast.geomorph.compare.multi.evol.rates, fast.geomorph.phylo.pls, fast.geomorph.procD.pgls , and fast.geomorph.physignal are covariance-baesd implementations of the geomorph functions compare.evol.rates, compare.multi.evol.rates, phylo.pls, procD.pgls, and physignal using a fast linear-time algorithm. Code is directly modified from the original geomorph code for direct comparison between distance-based and covariance-based methods.

Usage

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fast.geomorph.procD.pgls(f1, phy, iter = 1000,
int.first = FALSE, verbose = FALSE)

Arguments

f1

From geomorph: A formula for the linear model (e.g., y~x1+x2)

phy

From geomorph: A phylogenetic tree of class phylo

iter

From geomorph: Number of iterations for significance testing

int.first

From geomorph: A logical value to indicate if interactions of first main effects should precede subsequent main effects

verbose

From geomorph: A logical value specifying whether additional output should be displayed

Details

See procD.pgls

Value

See procD.pgls

Author(s)

Eric W. Goolsby

References

Goolsby E.W. 2016. Likelihood-Based Parameter Estimation for High-Dimensional Phylogenetic Comparative Models: Overcoming the Limitations of 'Distance-Based' Methods. In review.

Adams, D.C. 2014. A method for assessing phylogenetic least squares models for shape and other high-dimensional multivariate data. Evolution. 68:2675-2688.

See Also

procD.pgls

Examples

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### NOTE: this example is identical 
### to the example code for the
### analogous geomorph function
### for direct comparisons with
### 'fast.geomorph' phylocurve functions
require(geomorph)

### Example of D-PGLS for high-dimensional data
data(plethspecies)
Y.gpa<-gpagen(plethspecies$land)    #GPA-alignment
procD.pgls(Y.gpa$coords ~ Y.gpa$Csize,plethspecies$phy,iter=1000)
fast.geomorph.procD.pgls(Y.gpa$coords ~ Y.gpa$Csize,plethspecies$phy)

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