Extract residual correlations from gllvm object

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")
  }