Calculates the residual correlation matrix for gllvm model.

# S3 method for gllvm
getResidualCor(object, adjust = 1, site.index = NULL, ...)

Arguments

object

an object of class 'gllvm'.

adjust

The type of adjustment used for negative binomial and binomial distribution when computing residual correlation matrix. Options are 0 (no adjustment), 1 (the default adjustment) and 2 (alternative adjustment for NB distribution). See details.

site.index

A site index used used in the calculation of a GLLVM with quadratic response model, for which the residual correlations are calculated.

...

not used

Details

Residual correlation matrix is calculated based on the residual covariance matrix, see details from getResidualCov.gllvm.

Author

Francis K.C. Hui, Jenni Niku, David I. Warton

Examples

#'# Extract subset of the microbial data to be used as an example data(microbialdata) y <- microbialdata$Y[, order(colMeans(microbialdata$Y > 0), decreasing = TRUE)[21:40]] fit <- gllvm(y, family = poisson()) fit$logL
#> [1] -4242.667
cr <- getResidualCor(fit) cr[1:5,1:5]
#> OTU_79 OTU_164 OTU_462 OTU_95 OTU_833 #> OTU_79 1.0000000 0.3889616 0.3424165 -0.7493783 0.9956140 #> OTU_164 0.3889616 1.0000000 0.9987494 -0.9014804 0.4734446 #> OTU_462 0.3424165 0.9987494 1.0000000 -0.8787139 0.4288151 #> OTU_95 -0.7493783 -0.9014804 -0.8787139 1.0000000 -0.8080390 #> OTU_833 0.9956140 0.4734446 0.4288151 -0.8080390 1.0000000
if (FALSE) { # Load a dataset from the mvabund package data(antTraits) y <- as.matrix(antTraits$abund) # Fit gllvm model fit <- gllvm(y = y, family = poisson()) # residual correlations: cr <- getResidualCor(fit) # Plot residual correlations: install.packages("corrplot", "gclus") library(corrplot) library(gclus) corrplot(cr[order.single(cr), order.single(cr)], diag = F, type = "lower", method = "square", tl.cex = 0.8, tl.srt = 45, tl.col = "red") }