disttree {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).
disttree(formula, data, na.action, cluster, family = NO(), bd = NULL, type.tree = "mob", decorrelate = "none", offset, censtype = "none", censpoint = NULL, weights = NULL, terminal_objects = FALSE, type.hessian = c("checklist", "analytic", "numeric"), control = partykit::mob_control(), ocontrol = list(), ...)
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. |
na.action |
A function which indicates what should happen when the data
contain |
cluster |
An optional vector (typically numeric or factor) with a cluster ID to be employed for clustered covariances in the parameter stability tests. |
family |
specification of the response distribution.
Either a |
bd |
binomial denominator: additional parameter needed for binomial gamlss.families |
type.tree |
Specification of the type of tree learner, either
|
decorrelate |
Specification of the type of decorrelation for the
empirical estimating functions (or scores) either |
offset |
FIX ME. |
censtype |
Can either be 'none', 'left' or 'right' to set the type of censoring for censored response. |
censpoint |
Numeric value. Censoring point can be set for censored response. |
weights |
Optional numeric vector of case weights. |
terminal_objects |
Logical argument deciding whether a model object should be fit to the terminal node. |
type.hessian |
Can either be 'checklist', 'analytic' or 'numeric' to decide how the hessian matrix should be calculated in the fitting process in |
control |
|
ocontrol |
|
... |
Distributional regression trees are an application of model-based recursive partitioning and unbiased recursive partitioning
(implemented in mob
and ctree
) to parametric model fits
based on the GAMLSS family of distribtuions.
An object of S3 class disttree
inheriting from class modelparty
or constparty
.
tr <- disttree(dist ~ speed, data = cars) print(tr) plot(tr) plot(as.constparty(tr))