distextree {disttree} | R Documentation |
Trees based on maximum-likelihood estimation of parameters for distributions from the GAMLSS family (for generalized additive models for location, scale, and shape).
distextree(formula, data, subset, na.action = na.pass, weights, offset, cluster, family = NO(), control = distextree_control(...), converged = NULL, scores = NULL, doFit = TRUE, ...)
formula |
A symbolic description of the model to be fit. This
should be of type |
data |
An optional data frame containing the variables in the model. |
subset |
A sorted integer vector describing a subset. |
weights |
optional numeric vector of case weights. |
family |
specification of the response distribution.
Either a |
na.action |
A function which indicates what should happen when the data
contain |
offset |
FIX ME. |
cluster |
An optional vector (typically numeric or factor) with a cluster ID to be employed for clustered covariances in the parameter stability tests. |
control |
Control arguments passed to |
converged |
An optional function for checking user-defined criteria before splits are implemented. |
scores |
An optional named list of scores to be attached to ordered factors. |
doFit |
A logical indicating if the tree shall be grown (TRUE) or not FALSE |
... |
Distributional regression trees are an application of model-based recursive partitioning and unbiased recursive partitioning
(implemented in extree_fit
) to parametric model fits
based on the GAMLSS family of distribtuions.
An object of S3 class disttree
inheriting from class constparty
.
mob
, ctree
, extree_fit
, distfit
tr <- distextree(dist ~ speed, data = cars) print(tr) plot(tr) plot(as.constparty(tr))