gllvm: plot.gllvm – R documentation

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plot.gllvm

Plot Diagnostics for an gllvm Object

Description

Five plots (selectable by which) are currently available: a plot of residuals against linear predictors of fitted values, a Normal Q-Q plot of residuals with a simulated point-wise 95% confidence interval envelope, residuals against row index and column index and scale location plot.

Usage

## S3 method for class 'gllvm'
plot(
  x,
  which = 1:5,
  caption = c("Residuals vs linear predictors", "Normal Q-Q", "Residuals vs row index",
    "Residuals vs column index", "Scale-Location"),
  var.colors = NULL,
  add.smooth = TRUE,
  envelopes = TRUE,
  reps = 150,
  envelope.col = c("blue", "lightblue"),
  n.plot = NULL,
  ...
)

Arguments

`x`	an object of class 'gllvm'.
`which`	if a subset of the plots is required, specify a subset of the numbers 1:5, see caption below.
`caption`	captions to appear above the plots.
`var.colors`	colors for responses, vector with length of number of response variables or 1. Defaults to NULL, when different responses have different colors.
`add.smooth`	logical indicating if a smoother should be added.
`envelopes`	logical, indicating if simulated point-wise confidence interval envelope will be added to Q-Q plot, defaults to `TRUE`
`reps`	number of replications when simulating confidence envelopes for normal Q-Q plot
`envelope.col`	colors for envelopes, vector with length of two
`n.plot`	number of species (response variables) to be plotted. Defaults to `NULL` when all response variables are plotted. Might be useful when data is very high dimensional.
`...`	additional graphical arguments.

Details

plot.gllvm is used for model diagnostics. Dunn-Smyth residuals (randomized quantile residuals) (Dunn and Smyth, 1996) are used in plots. Colors indicate different species.

Author(s)

Jenni Niku <jenni.m.e.niku@jyu.fi>

References

Dunn, P. K., and Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236-244.

Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D., and Warton, D. I. (2015). Model-based approaches to unconstrained ordination. Methods in Ecology and Evolution, 6:399-411.

Examples

## Not run: 
## Load a dataset from the mvabund package
data(antTraits)
y <- as.matrix(antTraits$abund)
# Fit gllvm model with Poisson family
fit <- gllvm(y, family = poisson())
# Plot residuals
plot(fit, mfrow = c(3,2))

\donttest{
# Fit gllvm model with negative binomial family
fitnb <- gllvm(y = y, family = "negative.binomial")
# Plot residuals
plot(fitnb, mfrow = c(3,2))
# Plot only two first plots
plot(fitnb, which = 1:2, mfrow = c(1,2))
}

## End(Not run)

gllvm

Generalized Linear Latent Variable Models

v1.3.0

GPL-2

Authors

Jenni Niku [aut, cre], Wesley Brooks [aut], Riki Herliansyah [aut], Francis K.C. Hui [aut], Sara Taskinen [aut], David I. Warton [aut], Bert van der Veen [aut]

Initial release

2021-4-26