mob_control: Control Parameters for Model-Based Partitioning

Description

Various parameters that control aspects the fitting algorithm for recursively partitioned mob models.

Usage

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)

Arguments

alpha

numeric significance level. A node is splitted when the (possibly Bonferroni-corrected) $p$ value for any parameter stability test in that node falls below alpha (and the stopping criteria minsize and maxdepth

bonferroni

logical. Should $p$ values be Bonferroni corrected?

minsize, minsplit, minbucket

integer. The minimum number of observations in a node.
    If 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<

maxdepth

integer. The maximum depth of the tree.

mtry

integer. The number of partitioning variables randomly sampled
    as candidates in each node for forest-style algorithms. If mtry
    is greater than the number of partitioning variables, no random selection
    is performed. (Thus, by defau

trim

numeric. This specifies the trimming in the parameter instability
    test for the numerical variables. If smaller than 1, it is interpreted
    as the fraction relative to the current node size.

breakties

logical. Should ties in numeric variables be broken
    randomly for computing the associated parameter instability test?

parm

numeric or character. Number or name of model parameters
    included in the parameter instability tests (by default all parameters
    are included).

dfsplit

logical or numeric. as.integer(dfsplit) is the degrees of freedom
    per selected split employed when computing information criteria etc.

prune

character, numeric, or function for specifying post-pruning rule.
    If prune is NULL (the default), no post-pruning is performed.
    For likelihood-based mob() trees, prune can be set to
    "AI

restart

logical. When determining the optimal split point in a numerical
    variable: Should model estimation be restarted with NULL starting
    values for each split? The default is TRUE. If FALSE, then
    the parameter

verbose

logical. Should information about the fitting process
    of mob (such as test statistics, $p$ values, selected
    splitting variables and split points) be printed to the screen?

caseweights

logical. Should weights be interpreted as case weights?
    If TRUE, the number of observations is sum(weights),
    otherwise it is sum(weights > 0).

ytype, xtype

character. Specification of how mob should
    preprocess y and x variables. Possible choice are:
    "vector" (for y only), i.e., only one variable;
    "matrix", i.e., the mod

terminal, inner

character. Specification of which additional
    information ("estfun", "object", or both) should be
    stored in each node. If NULL, no additional information is
    stored.

model

logical. Should the full model frame be stored in the
    resulting object?

numsplit

character indicating how splits for numeric variables
    should be justified. Because any splitpoint in the interval between
    the last observation from the left child segment and the first observation
    from the right child segment leads to the same

catsplit

character indicating how (unordered) categorical variables
    should be splitted. By default the best "binary" split is
    searched (by minimizing the objective function). Alternatively,
    if set to "multiway", the node is si

vcov

character indicating which type of covariance matrix
    estimator should be employed in the parameter instability tests.
    The default is the outer product of gradients ("opg").
    Alternatively, vcov = "info" employs the inf

ordinal

character indicating which type of parameter instability
    test should be employed for ordinal partitioning variables (i.e.,
    ordered factors). This can be "chisq", "max", or "L2".
    If "chisq" th

nrep

numeric. Number of replications in the simulation of
    p-values for the ordinal "L2" statistic (if used).

applyfun

an optional 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 lappl

cores

numeric. If set to an integer the applyfun is set to
    mclapply with the desired number of cores.

`Value`

A list of class mob_control containing the control parameters.

`Details`

See 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).

`See Also`

mob