RSProjection {evolqg}

Random Skewers projection

Description

Uses Bayesian posterior samples of a set of covariance matrices to identify directions of the morphospace in which these matrices differ in their amount of genetic variance.

Usage

RSProjection(cov.matrix.array, p = 0.95, num.vectors = 1000)

PlotRSprojection(rs_proj, cov.matrix.array, p = 0.95, ncols = 5)

Arguments

cov.matrix.array

Array with dimensions traits x traits x populations x MCMCsamples

p

significance threshold for comparison of variation in each random direction

num.vectors

number of random vectors

rs_proj

output from RSProjection

ncols

number of columns in plot

Value

projection of all matrices in all random vectors

set of random vectors and confidence intervals for the projections

eigen decomposition of the random vectors in directions with significant differences of variations

References

Aguirre, J. D., E. Hine, K. McGuigan, and M. W. Blows. "Comparing G: multivariate analysis of genetic variation in multiple populations." Heredity 112, no. 1 (2014): 21-29.

Examples

# small MCMCsample to reduce run time, acctual sample should be larger
data(dentus)
cov.matrices = dlply(dentus, .(species), function(x) lm(as.matrix(x[,1:4])~1)) |>
               laply(function(x) BayesianCalculateMatrix(x, samples = 50)$Ps)
cov.matrices = aperm(cov.matrices, c(3, 4, 1, 2))

rs_proj = RSProjection(cov.matrices, p = 0.8)
PlotRSprojection(rs_proj, cov.matrices, ncol = 5)