A given data set is subdivided into three types of variables: explanatory, intermediate and response variables.
Here, each specified intermediate variable is modelled separately
following pFUN
, a list of lists with elements specifying an
arbitrary number of models for the intermediate variables and an
optional element training.set = c("oob", "bag", "all")
. The
element training.set
determines whether, predictive models for
the intermediate are calculated based on the out-of-bag sample
("oob"
), the default, on the bag sample ("bag"
) or on all
available observations ("all"
). The elements of pFUN
,
specifying the models for the intermediate variables are lists as
described in inclass
.
Note that, if no formula is given in these elements, the functional
relationship of formula
is used.
The response variable is modelled following cFUN
.
This can either be a fixed classifying function as described in Peters
et al. (2003) or a list,
which specifies the modelling technique to be applied. The list
contains the arguments model
(which model to be fitted),
predict
(optional, how to predict), formula
(optional, of
type y~w1+w2+w3+x1+x2
determines the variables the classifying
function is based on) and the optional argument training.set =
c("fitted.bag", "original", "fitted.subset")
specifying whether the classifying function is trained on the predicted
observations of the bag sample ("fitted.bag"
),
on the original observations ("original"
) or on the
predicted observations not included in a defined subset
("fitted.subset"
). Per default the formula specified in
formula
determines the variables, the classifying function is
based on.
Note that the default of cFUN = list(model = NULL, training.set = "fitted.bag")
uses the function rpart
and
the predict function predict(object, newdata, type = "class")
.