cancor.phylog {PHYLOGR}

Canonical Correlation Analysis from Simulated Data Sets

Description

Performs a canonical correlation analysis on two sets of simulated data and returns the canonical correlations.

Usage

cancor.phylog(data1, data2, max.num=0, exclude.tips=NULL,lapply.size=100,
              xcenter=TRUE, ycenter=TRUE)

Arguments

data1

the columns from a data set returned from read.sim.data that you want to use as the first set in the canonical correlation analysis. The first column MUST be sim.counter and the second Tips.

data2

the columns from a data set returned from read.sim.data that you want to use as the second set in the canonical correlation analysis. The first column MUST be sim.counter and the second Tips.

max.num

if different from 0, the maximum number of simulations to analyze.

exclude.tips

an optional vector giving the names of tips to exclude from the analyses.

lapply.size

a tuning parameter that can affect the speed of calculations; see Details in phylog.lm.

xcenter

should the x columns be centered? defaults to yes; see cancor.

ycenter

should the y columns be centered? defaults to yes; see cancor.

Value

A list (of class phylog.cancor) with components

call

the call to the function

CanonicalCorrelations

the canonical correlations. The one with sim.counter=0 corresponds to the original (”real”) data.

WARNING

It is necessary to be careful with the null hypothesis you are testing and how the null data set —the simulations— are generated. For instance, suppose you want to examine the canonical correlations between sets x and y; you will probably want to generate x and y each with the observed correlations within each set so that the correlations within each set are maintained (but with no correlations among sets). You probably do not want to generate each of the x's as if they were independent of each other x, and ditto for y, since that will destroy the correlations within each set; see some discussion in Manly, 1997.

Author(s)

Ramon Diaz-Uriarte and Theodore Garland, Jr.

References

Diaz-Uriarte, R., and Garland, T., Jr., in prep. PHYLOGR: an R package for the analysis of comparative data via Monte Carlo simulations and generalized least squares approaches.

Krzanowski, W. J. (1990) Principles of multivariate analysis Oxford University Press.

Manly,B. F. J. (1997) Randomization, bootstraping, and Monte Carlo methods in biology, 2nd ed. Chapman & Hall.

Morrison, D. F. (1990) Multivariate statistcal methods, 3rd ed. McGraw-Hill.

See Also

read.sim.data, summary.phylog.cancor

Examples

data(SimulExample)
ex1.cancor <- cancor.phylog(SimulExample[,c(1,2,3,4,5)],SimulExample[,c(1,2,6,7,8)])
ex1.cancor
summary(ex1.cancor)
plot(ex1.cancor)