SRD {evolqg}

Compare matrices via Selection Response Decomposition

Description

Based on Random Skewers technique, selection response vectors are expanded in direct and indirect components by trait and compared via vector correlations.

Usage

SRD(cov.x, cov.y, ...)

## Default S3 method:
SRD(cov.x, cov.y, iterations = 1000, ...)

## S3 method for class 'list'
SRD(cov.x, cov.y = NULL, iterations = 1000, parallel = FALSE, ...)

## S3 method for class 'SRD'
plot(x, matrix.label = "", ...)

Arguments

cov.x

Covariance matrix being compared. cov.x can be a matrix or a list.

cov.y

Covariance matrix being compared. Ignored if cov.x is a list.

...

additional parameters passed to other methods

iterations

Number of random vectors used in comparison

parallel

if TRUE computations are done in parallel. Some foreach back-end must be registered, like doParallel or doMC.

x

Output from SRD function, used in plotting

matrix.label

Plot label

Details

Output can be plotted using PlotSRD function

Value

List of SRD scores means, confidence intervals, standard deviations, centered means e centered standard deviations

pc1 scored along the pc1 of the mean/SD correlation matrix

model List of linear model results from mean/SD correlation. Quantiles, interval and divergent traits

Note

If input is a list, output is a symmetric list array with pairwise comparisons.

Author(s)

Diogo Melo, Guilherme Garcia

References

Marroig, G., Melo, D., Porto, A., Sebastiao, H., and Garcia, G. (2011). Selection Response Decomposition (SRD): A New Tool for Dissecting Differences and Similarities Between Matrices. Evolutionary Biology, 38(2), 225-241. doi:10.1007/s11692-010-9107-2

See Also

RandomSkewers

Examples

cov.matrix.1 <- cov(matrix(rnorm(30*10), 30, 10))
cov.matrix.2 <- cov(matrix(rnorm(30*10), 30, 10))
colnames(cov.matrix.1) <- colnames(cov.matrix.2) <- sample(letters, 10)
rownames(cov.matrix.1) <- rownames(cov.matrix.2) <- colnames(cov.matrix.1)
srd.output <- SRD(cov.matrix.1, cov.matrix.2)

#lists
m.list <- RandomMatrix(10, 4)
srd.array.result = SRD(m.list)

#divergent traits
colnames(cov.matrix.1)[as.logical(srd.output$model$code)]

#Plot
plot(srd.output)

## For the array generated by SRD(m.list) you must index the idividual positions for plotting:
plot(srd.array.result[1,2][[1]])
plot(srd.array.result[3,4][[1]])

## Not run: 
#Multiple threads can be used with some foreach backend library, like doMC or doParallel
library(doMC)
registerDoMC(cores = 2)
SRD(m.list, parallel = TRUE)

## End(Not run)