mob models.
mob_control(alpha = 0.05, bonferroni = TRUE, minsize = NULL, maxdepth = Inf, mtry = Inf, trim = 0.1, breakties = FALSE, parm = NULL, dfsplit = TRUE, prune = NULL, restart = TRUE, verbose = FALSE, caseweights = TRUE, ytype = "vector", xtype = "matrix", terminal = "object", inner = terminal, model = TRUE, numsplit = "left", catsplit = "binary", vcov = "opg", ordinal = "chisq", nrep = 10000, minsplit = minsize, minbucket = minsize, applyfun = NULL, cores = NULL)alpha (and the stopping
criteria minsize and maxdepth are not fulfilled).NULL, the default is to use 10 times the number of parameters
to be estimated (divided by the number of responses per observation
if that is greater than 1). minsize is the recommended name and
minsplit/minbucket are only included for backward compatibility with previous
versions of mob and compatibility with ctree, respectively.mtry
is greater than the number of partitioning variables, no random selection
is performed. (Thus, by default all available partitioning variables are considered.)as.integer(dfsplit) is the degrees of freedom
per selected split employed when computing information criteria etc.prune is NULL (the default), no post-pruning is performed.
For likelihood-based mob() trees, prune can be set to
"AIC" or "BIC" for post-pruning based on the corresponding
information criteria. More general rules (also in scenarios that are
not likelihood-based), can be specified by function arguments to
prune, for details see below.NULL starting
values for each split? The default is TRUE. If FALSE, then
the parameter estimates from the previous split point are used as starting
values for the next split point (because in practice the difference are
often not huge). (Note that in that case a for loop is used
instead of the applyfun for fitting models across sample splits.)mob (such as test statistics, $p$ values, selected
splitting variables and split points) be printed to the screen?TRUE, the number of observations is sum(weights),
otherwise it is sum(weights > 0).mob should
preprocess y and x variables. Possible choice are:
"vector" (for y only), i.e., only one variable;
"matrix", i.e., the model matrix of all variables;
"data.frame", i.e., a data frame of all variables."estfun", "object", or both) should be
stored in each node. If NULL, no additional information is
stored.mob_control:
Either, the split is "left"-justified (the default for
backward compatibility) or "center"-justified using the
midpoint of the possible interval."binary" split is
searched (by minimizing the objective function). Alternatively,
if set to "multiway", the node is simply splitted into all
levels of the categorical variable."opg").
Alternatively, vcov = "info" employs the information matrix
and vcov = "sandwich" the sandwich matrix (both of which are
only sensible for maximum likelihood estimation)."chisq", "max", or "L2".
If "chisq" then the variable is treated as unordered and a
chi-squared test is performed. If "L2", then a maxLM-type
test as for numeric variables is carried out but correcting for ties.
This requires simulation of p-values via catL2BB
and requires some computation time. For "max" a weighted
double maximum test is used that computes p-values via
pmvnorm."L2" statistic (if used).lapply-style function with arguments
function(X, FUN, ...). It is used for refitting the model across
potential sample splits. The default is to use the basic lapply
function unless the cores argument is specified (see below).applyfun is set to
mclapply with the desired number of cores.mob_control containing the control parameters.
mob for more details and references.
For post-pruning, prune can be set to a function(objfun, df, nobs)
which either returns TRUE to signal that a current node can be pruned
or FALSE. All supplied arguments are of length two: objfun is the sum of objective
function values in the current node and its child nodes, respectively.
df is the degrees of freedom in the current node and its child nodes,
respectively. nobs is vector with the number of observations in the
current node and the total number of observations in the dataset, respectively. If the objective function employed in the mob() call is the negative
log-likelihood, then a suitable function is set up on the fly by comparing
(2 * objfun + penalty * df) in the current and the daughter nodes.
The penalty can then be set via a numeric or character value for prune:
AIC is used if prune = "AIC" or prune = 2 and
BIC if prune = "BIC" or prune = log(n).
mob