Extract prediction errors for latent variables from gllvm object

Calculates the prediction errors for latent variables and random effects for gllvm model.

# S3 method for gllvm
getPredictErr(object, CMSEP = TRUE, cov = FALSE, ...)

Arguments

object: an object of class 'gllvm'.
CMSEP: logical, if TRUE conditional mean squared errors for predictions are calculated. If FALSE, prediction errors are based on covariances of the variational distributions for method ="VA" and method ="EVA".
cov: if TRUE, return as covariances/variances of predictions. Otherwise FALSE (default) return as standard errors of predictions.
...: not used

Value

Function returns following components:

lvs: prediction errors for latent variables
row.effects: prediction errors for random row effects if included

Details

Calculates conditional mean squared errors for predictions. If variational approximation is used, prediction errors can be based on covariances of the variational distributions, and therefore they do not take into account the uncertainty in the estimation of (fixed) parameters.

Author

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

Examples

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())
# prediction errors for latent variables:
getPredictErr(fit)
}