Usage
tvcm_control(minsize = 30, mindev = ifelse(sctest, 0.0, 2.0), sctest = FALSE, alpha = 0.05, bonferroni = TRUE, trim = 0.1, estfun.args = list(), nimpute = 5, maxnomsplit = 5, maxordsplit = 9, maxnumsplit = 9, maxstep = 1e3, maxwidth = Inf, maxdepth = Inf, lossfun = neglogLik2, ooblossfun = NULL, fast = TRUE, cp = 0.0, dfpar = 0.0, dfsplit = 1.0, cv = !sctest, folds = folds_control("kfold", 5), prune = cv, papply = mclapply, papply.args = list(), center = fast, seed = NULL, verbose = FALSE, ...)
Arguments
alpha, bonferroni, trim, estfun.args, nimpute
mindev, cv, folds, prune, center
minsize
numeric (vector). The minimum sum of weights in
terminal nodes.
sctest
logical scalar. Defines whether coefficient constancy
tests should be used for the variable and node selection in each
iteration.
maxnomsplit
integer. For nominal partitioning variables with
more the maxnomsplit the categories are ordered an treated as
ordinal.
maxordsplit
integer. The maximum number of splits of ordered
partitioning variables to be evaluated.
maxnumsplit
integer. The maximum number of splits of numeric
partitioning variables to be evaluated.
maxstep
integer. The maximum number of iterations i.e. number
of splits to be processed.
maxwidth
integer (vector). The maximum width of the partition(s).
maxdepth
integer (vector). The maximum depth of the partition(s).
lossfun
a function to extract the training error, typically
minus two times the negative log likelihood of the fitted model (see
neglogLik2). Is currently ignored if a glm model is
fitted and fast = TRUE. ooblossfun
a loss function that defines how to compute the
validation error during cross-validation. The function will be
assigned to the fun argument of oobloss. fast
logical scalar. Whether the approximative model should be
used to search for the next split. The approximative search model
uses only the observations of the node to split and incorporates the
fitted values of the current model as offsets. Therewith the
estimation is reduces to the coefficients of the added split. If
FALSE, the accurate search model is used.
cp
numeric scalar. The penalty to be multiplied with the
complexity of the model during partitioning. The complexity of the
model is defined as the number of coefficients times dfpar
plus the number of splits times dfsplit. By default, cp
= 0 (no penalization during partitioning) and dfpar = 0 and
dfsplit = 1 (the complexity is measured as the total number
of splits). cp also presents the minimum evaluated value at
cross-validation.
dfpar
numeric scalar. The degree of freedom per model
coefficient. Is used to compute the complexity of the model, see
cp.
dfsplit
a numeric scalar. The degree of freedom per split. Is
used to compute the complexity of the model, see cp.
papply
(parallel) apply function, defaults to
mclapply. The function will parallelize the
partition stage and the evaluation of the cross-validation folds as
well as the final pruning stage. papply.args
a list of arguments to be passed to papply.
seed
an integer specifying which seed should be set at the
beginning.
verbose
logical. Should information about the fitting process
be printed to the screen?
...
further, undocumented arguments to be passed.