distforest {disttree} | R Documentation |
Forests based on maximum-likelihood estimation of parameters for distributions from the GAMLSS family (for generalized additive models for location, scale, and shape).
distforest(formula, data, na.action = na.pass, cluster, family = NO(), bd = NULL, type.tree = "ctree", decorrelate = "none", offset, censtype = "none", censpoint = NULL, weights = NULL, control = partykit::ctree_control(teststat = "quad", testtype = "Univ", mincriterion = 0, ...), ocontrol = list(), type.hessian = c("checklist", "analytic", "numeric"), ntree = 500L, fit = TRUE, perturb = list(replace = FALSE, fraction = 0.632), fitted.OOB = TRUE, cores = NULL, applyfun = NULL, mtry = ceiling(sqrt(nvar)), ...)
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. |
control |
|
ocontrol |
List with control parameters passed to
|
type.hessian |
Can either be 'checklist', 'analytic' or 'numeric' to decide how the hessian matrix should be calculated in the fitting process in |
ntree |
Number of trees to grow for the forest. |
fit |
logical. if TRUE, fitted and predicted values and predicted parameters are calculated for the learning data (together with loglikelihood) |
perturb |
a list with arguments |
fitted.OOB |
logical. if fitted.OOB=TRUE the weights for each observation of the learning data are predicted by |
cores |
numeric. If set to an integer the |
applyfun |
an optional |
mtry |
number of input variables randomly sampled as candidates
at each node for random forest like algorithms. Bagging, as special case
of a random forest without random input variable sampling, can
be performed by setting |
... |
Distributional regression forests are an application of model-based recursive partitioning
(implemented in mob
, ctree
and cforest
) to parametric model fits based on the GAMLSS family of distribtuions.
An object of S3 class distforest
inheriting from class cforest
.
df <- distforest(dist ~ speed, data = cars) predict(df)