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

models.

1 2 3 4 5 6 | ```
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` |
numeric significance level. A node is splitted when
the (possibly Bonferroni-corrected) |

`bonferroni` |
logical. Should |

`minsize, minsplit, minbucket` |
integer. The minimum number of observations in a node.
If |

`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 |

`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. |

`prune` |
character, numeric, or function for specifying post-pruning rule.
If |

`restart` |
logical. When determining the optimal split point in a numerical
variable: Should model estimation be restarted with |

`verbose` |
logical. Should information about the fitting process
of |

`caseweights` |
logical. Should weights be interpreted as case weights?
If |

`ytype, xtype` |
character. Specification of how |

`terminal, inner` |
character. Specification of which additional
information ( |

`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 observed split, two
options are available in |

`catsplit` |
character indicating how (unordered) categorical variables
should be splitted. By default the best |

`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 ( |

`ordinal` |
character indicating which type of parameter instability
test should be employed for ordinal partitioning variables (i.e.,
ordered factors). This can be |

`nrep` |
numeric. Number of replications in the simulation of
p-values for the ordinal |

`applyfun` |
an optional |

`cores` |
numeric. If set to an integer the |

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)`

.

A list of class `mob_control`

containing the control parameters.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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