gllvm (version 2.0.5)

summary.gllvm: Summarizing gllvm model fits

Description

A summary of the fitted 'gllvm' object, including function call, distribution family and model parameters.

Usage

# S3 method for gllvm
summary(
  object,
  by = "all",
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  dispersion = FALSE,
  spp.intercepts = FALSE,
  row.intercepts = FALSE,
  Lvcoefs = FALSE,
  rotate = TRUE,
  type = NULL,
  ...
)
# S3 method for summary.gllvm
print(x, ...)
# S3 method for summary.gllvm
plot(x, component = NULL, ...)

Arguments

object

an object of class 'gllvm'

by

By = "all" (default) will return a Wald statistics per predictor and LV if the ordination includes predictors, by = "terms" will return a multivariate Wald statistic per predictor (displayed at first LV), and by = "LV" will do the same but per dimension (displayed at first predictors).

digits

the number of significant digits to use when printing

signif.stars

If TRUE, significance stars are printed for each coefficient, defaults to TRUE

dispersion

option to return dispersion parameters, defaults to FALSE

spp.intercepts

option to return species intercepts, defaults to FALSE

row.intercepts

option to return row intercepts, defaults to FALSE

Lvcoefs

option to return species scores in the ordination, defaults to FALSE. Returns species optima for quadratic model.

rotate

defaults to TRUE. If TRUE rotates the output of the latent variables to principal direction, so that it coincides with the ordiplot results. If both unconstrained and constrained latent variables are included, predictor slopes are not rotated.

type

to match "type" in ordiplot.gllvm

...

not used.

x

a summary object

component

component to be plotted

Author

Jenni Niku <[e-mail]>, Bert van der Veen

Details

Various options are available to include extra parameter estimates in the summary, which have been excluded by default, for readability.

Examples

if (FALSE) {
## 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())
summary(fit)

## Plot canonical coefficients
X = antTraits$env
fit2 <- gllvm(y = y, X = X, family = "negative.binomial", num.RR = 2)
plot(summary(fit2))
}