| plotqrrvglm {VGAM} | R Documentation |
The residuals of a QRR-VGLM are plotted for model diagnostic purposes.
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, ...)
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). |
Plotting the residuals can be potentially very useful for checking that the model fit is adequate.
The original object.
An ordination plot of a QRR-VGLM can be obtained
by lvplot.qrrvglm.
Thomas W. Yee
Yee, T. W. (2004) A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685–701.
## Not run:
# QRR-VGLM on the hunting spiders data
# This is computationally expensive
data(hspider)
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)
## End(Not run)