plotqrrvglm: Model Diagnostic Plots for QRR-VGLMs

Description

The residuals of a QRR-VGLM are plotted for model diagnostic purposes.

Usage

plotqrrvglm(object, 
            rtype = c("pearson", "response", "deviance", "working"), 
            ask = FALSE, 
            main = paste(Rtype, "residuals vs latent variable(s)"), 
            xlab = "Latent Variable", 
            ITolerances = object@control$EqualTolerances, ...)

Arguments

object

An object of class "qrrvglm".

rtype

Character string giving residual type. By default, the first one is chosen.

ask

Logical. If TRUE, the user is asked to hit the return key for the next plot.

main

Character string giving the title of the plot.

xlab

Character string giving the x-axis caption.

ITolerances

Logical. This argument is fed into Coef(object, ITolerances=ITolerances).

...

Other plotting arguments (see par).

Value

The original object.

Details

Plotting the residuals can be potentially very useful for checking that the model fit is adequate.

References

Yee, T. W. (2004) A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685--701.

Examples

Run this code

# QRR-VGLM on the hunting spiders data
# This is computationally expensive
set.seed(111)  # This leads to the global solution
# hspider[,1:6]=scale(hspider[,1:6]) # Standardize the environmental variables
p1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi,
               Auloalbi, Pardlugu, Pardmont, Pardnigr, Pardpull,
               Trocterr, Zoraspin) ~
         WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,
         fam = quasipoissonff, data = hspider, Crow1positive=FALSE)
par(mfrow=c(3,4)) 
plot(p1, rtype="d", col="blue", pch=4, las=1)

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