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 |
rtype |
Character string giving residual type. By default, the first one is chosen. |
ask |
Logical. If |
main |
Character string giving the title of the plot. |
xlab |
Character string giving the x-axis caption. |
ITolerances |
Logical. This argument is fed into
|
... |
Other plotting arguments (see |
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 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)