View source: R/boost_tree-mboost.R
blackboost_train | R Documentation |
blackboost_train()
is a wrapper for the blackboost()
function in the
mboost package that fits tree-based models
where all of the model arguments are in the main function.
blackboost_train(
formula,
data,
family,
weights = NULL,
teststat = "quadratic",
testtype = "Teststatistic",
mincriterion = 0,
minsplit = 10,
minbucket = 4,
maxdepth = 2,
saveinfo = FALSE,
...
)
formula |
A symbolic description of the model to be fitted. |
data |
A data frame containing the variables in the model. |
family |
A |
weights |
An optional vector of weights to be used in the fitting process. |
teststat |
A character specifying the type of the test statistic to be applied for variable selection. |
testtype |
A character specifying how to compute the distribution of
the test statistic. The first three options refer to p-values as criterion,
|
mincriterion |
The value of the test statistic or 1 - p-value that must be exceeded in order to implement a split. |
minsplit |
The minimum sum of weights in a node in order to be considered for splitting. |
minbucket |
The minimum sum of weights in a terminal node. |
maxdepth |
The maximum depth of the tree. The default |
saveinfo |
Logical. Store information about variable selection procedure in info slot of each partynode. |
... |
Other arguments to pass. |
A fitted blackboost model.
blackboost_train(Surv(time, status) ~ age + ph.ecog,
data = lung[-14, ], family = mboost::CoxPH()
)
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