Predict/Fitted Values for HXLR Fits
Obtains various types of predictions/fitted values for heteroscedastic extended logistic regression (HXLR) models.
# S3 method for hxlr predict(object, newdata = NULL, type = c("class", "probability", "cumprob", "location", "scale"), thresholds = object$thresholds, na.action = na.pass, …) # S3 method for hxlr fitted(object, type = c("class", "probability", "cumprob", "location", "scale"), …)
an object of class
an optional data frame in which to look for variables with which to predict.
type of prediction:
"probability"returns a data frame with category probabilities,
"cumprob"returns cumulative probabilities,
"scale"return the location and scale of the predicted latent distribution respectively, and
"class"returns the category with the highest probability. Default is
optional thresholds used for defining the thresholds for types
"class". Can differ from thresholds used for fitting. If omitted, the same thresholds as for fitting are used.
A function which indicates what should happen when the data contain
NAs. Default is na.pass
further arguments passed to or from other methods.
"prob" a matrix with number of intervals (= number of
thresholds + 1) columns is produced. Each row corresponds to a row in newdata
and contains the predicted probabilities to fall in the corresponding interval.
"cumprob" a matrix with number of thresholds columns is
produced. Each row corresponds to a row in
newdata and contains the
predicted probabilities to fall below the corresponding threshold.
"scale" a vector is
returned respectively with either the most probable categories (
or the location (
"location") or scale (
scale) of the latent