| predict.qrrvglm {VGAM} | R Documentation |
Predicted values based on a constrained quadratic ordination (CQO) object.
predict.qrrvglm(object, newdata=NULL,
type=c("link", "response", "lv", "terms"),
se.fit=FALSE, deriv=0, dispersion=NULL,
extra=object@extra, varlvI = FALSE, reference = NULL, ...)
object |
Object of class inheriting from "qrrvglm". |
newdata |
An optional data frame in which to look for variables with which
to predict. If omitted, the fitted linear predictors are used.
|
type, se.fit, dispersion, extra |
See predict.vglm.
|
deriv |
Derivative. Currently only 0 is handled. |
varlvI, reference |
Arguments passed into Coef.qrrvglm.
|
... |
Currently undocumented. |
Obtains predictions from a fitted CQO object. Currently there are lots of limitations of this function; it is unfinished.
See predict.vglm.
This function is not robust and has not been checked fully.
T. W. Yee
Yee, T. W. (2004) A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685–701.
cqo.
data(hspider)
hspider[,1:6]=scale(hspider[,1:6]) # Standardize the environmental variables
set.seed(1234)
p1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi, Auloalbi,
Pardlugu, Pardmont, Pardnigr, Pardpull, Trocterr, Zoraspin) ~
WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,
fam=poissonff, data=hspider, Crow1positive=FALSE, ITol=TRUE)
sort(p1@misc$deviance.Bestof) # A history of all the iterations
predict(p1)[1:3,]
# The following should be all zeros
max(abs(predict(p1, new=hspider[1:3,]) - predict(p1)[1:3,]))
max(abs(predict(p1, new=hspider[1:3,], type="res") - fitted(p1)[1:3,]))