All functions

AICc(<gllvm>) nobs(<gllvm>)

Corrected Akaike information criterion and number of observations

anova(<gllvm>)

Analysis Of Deviance for gllvm

beetle

ground beetle assemblages

coefplot(<gllvm>)

Plot covariate coefficients and confidence intervals

confint(<gllvm>)

Confidence intervals for model parameters

optima(<gllvm>) tolerances(<gllvm>)

Functions to extract ecological quantities of the latent variables from a GLLVM, if species are a quadratic function of the latent variables.

eSpider

Hunting spider data

fungi

Wood-decaying fungi data

getEnvironCov(<gllvm>)

Extract species covariances due to environmental random effects from gllvm object

getLoadings(<gllvm>)

Extract loadings

getLV(<gllvm>)

Extract latent variables

getPredictErr(<gllvm>)

Extract prediction errors for latent variables from gllvm object

getResidualCor(<gllvm>)

Extract residual correlations from gllvm object

getResidualCov(<gllvm>)

Extract residual covariance matrix from gllvm object

gllvm()

Generalized Linear Latent Variable Models

goodnessOfFit()

Goodness of fit measures for a gllvm

kelpforest

Kelp Forest community Dynamics: Cover of sessile organisms, Uniform Point Contact

logLik(<gllvm>)

Log-likelihood of gllvm

microbialdata

Microbial community data

ordiplot(<gllvm>)

Plot latent variables from gllvm model

phyloplot(<gllvm>)

Plot phylogenetic random effects from gllvm

plot(<gllvm>)

Plot Diagnostics for an gllvm Object

predict(<gllvm>)

Predict Method for gllvm Fits

predictLVs(<gllvm>)

Predict latent variables for gllvm Fits

randomCoefplot(<gllvm>)

Plot random slope coefficients

residuals(<gllvm>)

Dunn-Smyth residuals for gllvm model

se(<gllvm>)

Standard errors for gllvm model

simulate(<gllvm>)

Simulate data from gllvm fit

Skabbholmen

Skabbholmen island data

summary(<gllvm>) print(<summary.gllvm>) plot(<summary.gllvm>)

Summarizing gllvm model fits

update(<gllvm>)

Update and Re-fit a gllvm Model Call

vcov(<gllvm>)

Returns variance-covariance matrix of coefficients in a GLLVM.

VP(<gllvm>) print(<VP.gllvm>) plotVarPartitioning() plotVP() plot(<VP.gllvm>)

Calculate variance partitioning