R/getResidualCor.gllvm.R
getResidualCor.gllvm.Rd
Calculates the residual correlation matrix for gllvm model.
# S3 method for class 'gllvm'
getResidualCor(object, adjust = 1, x = NULL, ...)
an object of class 'gllvm'.
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.
(optional) vector of covariate values to calculate the covariance for, when applicable.
not used
Residual correlation matrix is calculated based on the residual covariance matrix, see details from getResidualCov.gllvm
.
#'# 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")
} # }