Extract residual correlations from gllvm object — getResidualCor.gllvm • gllvm

Calculates the residual correlation matrix for gllvm model.

# S3 method for class 'gllvm'
getResidualCor(object, adjust = 1, x = 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.
x: (optional) vector of covariate values to calculate the covariance for, when applicable.
...: 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$logL
#> [1] -4242.667
cr <- getResidualCor(fit)
cr[1:5,1:5]
#>             OTU_79    OTU_164    OTU_462     OTU_95    OTU_833
#> OTU_79   1.0000000  0.3890406  0.3425103 -0.7495015  0.9956110
#> OTU_164  0.3890406  1.0000000  0.9987501 -0.9014370  0.4735480
#> OTU_462  0.3425103  0.9987501  1.0000000 -0.8786728  0.4289339
#> OTU_95  -0.7495015 -0.9014370 -0.8786728  1.0000000 -0.8081673
#> OTU_833  0.9956110  0.4735480  0.4289339 -0.8081673  1.0000000
if (FALSE) { # \dontrun{
# Load a dataset from the mvabund package
data(antTraits, package = "mvabund")
y <- as.matrix(antTraits$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")
  } # }