getPredictErr.gllvm: Extract prediction errors for latent variables from gllvm...

In gllvm: Generalized Linear Latent Variable Models

Extract prediction errors for latent variables from gllvm object

Description

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

Usage

## S3 method for class '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

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.

Value

Function returns following components:

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

Author(s)

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

Examples

## Not run: 
# 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)

## End(Not run)

gllvm documentation built on Sept. 18, 2023, 5:22 p.m.