spaMM: plot.HL – R documentation

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plot.HL

Model checking plots for mixed models

Description

This function provides diagnostic plots for residual errors from the mean model and for random effects. Plots for the mean models are similar to those for GLMs. They use standardized deviance residuals as described by Lee et al. (2006, p.52). This means that plots for residual errors use the residuals provided by residuals(<fit object>, type="std_dev_res"); and that plots for random effects likewise consider standardized values.

Usage

## S3 method for class 'HLfit'
plot(x, which = c("mean", "ranef"), 
      titles = list(
          meanmodel=list(outer="Mean model",devres="Deviance residuals", 
                         absdevres="|Deviance residuals|", resq="Residual quantiles", 
                         devreshist="Deviance residuals"), 
          ranef=list(outer="Random effects and leverages",qq="Random effects Q-Q plot", 
                     levphi=expression(paste("Leverages for ",phi)), 
          levlambda=expression(paste("Leverages for ",lambda))) 
        ), 
      control= list(), ask=TRUE, ...)

Arguments

`x`	An object of class `HLfit`, as returned by the fitting functions in `spaMM`.
`which`	A vector of keywords for different types of plots. By default, two types of plots are presented on different devices: diagnostic plots for mean values, and diagnostic plots for random effects. Either one can be selected using this argument. Use keyword `"predict"` for a plot of predicted response against actual response.
`titles`	A list of the `main` (inner and outer) titles of the plots. See the default value for the format.
`control`	A list of default options for the plots. Defaults are `pch="+"` and `pcol="blue"` for points, and `lcol="red"` for curves.
`ask`	Logical; passed to `devAskNewPage` which is run when a new device is opened by `code.HLfit`.
`...`	Options passed from `plot.HLfit` to `par`.

Details

In principle the standardized deviance residuals for the mean model should have a nearly Gaussian distribution hence form a nearly straight line on a Q-Q plot. However this is (trivially) not so for well-specified (nearly-)binary response data nor even for well-specified Poisson response data with moderate expectations. Hence this plot is not so useful. The DHARMa package proposes better-behaved diagnostic plots (but the p-value that appears on one of these plots may not stand for a valid goodness-of-fit test). The current version of DHARMa should handle spaMM fit objects; otherwise, see https://github.com/florianhartig/DHARMa/issues/95 for how to run DHARMa procedures on spaMM output.

Value

Returns the input object invisibly.

References

Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalized linear models with random effects: unified analysis via h-likelihood. Chapman & Hall: London.

Examples

data("blackcap")
fit <- fitme(migStatus ~ 1+ Matern(1|longitude+latitude),data=blackcap,
             fixed=list(lambda=1,nu=1,rho=1))
plot(fit)

spaMM

Mixed-Effect Models, with or without Spatial Random Effects

v3.10.0

CeCILL-2

Authors

François Rousset [aut, cre, cph] (<https://orcid.org/0000-0003-4670-0371>), Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut] (<https://orcid.org/0000-0003-0637-2959>), GSL authors [ctb] (src/gsl_bessel.*)

Initial release

2022-02-06