Usage
vim.snp(object, useN = NULL, iter = NULL, standardize = NULL, mu = 0, addMatImp = FALSE, prob.case = 0.5, rand = NA)
vim.set(object, set = NULL, useN = NULL, iter = NULL, standardize = NULL, mu = 0, addMatImp = FALSE, prob.case = 0.5, rand = NA)
Arguments
object
an object of class logicBagg, i.e.\ the output of logic.bagging.
set
either a list or a character or numeric vector.
If NULL (default), then it
will be assumed that data, i.e.\ the data set used in the application of logic.bagging,
has been generated using make.snp.dummy or similar functions for coding variables
by binary variables, i.e.\ with a function that splits a variable, say SNPx, into the dummy variables
SNPx.1, SNPx.2, ... (where the ``." can also be any other sign, e.g., an underscore).
If a character or a numeric vector,
then the length of set must be equal to the number of variables used in object,
i.e.\ the number of columns of data in the logicBagg object, and must specify
the set to which a variable belongs either by an integer between 1 and the number of sets, or
by a set name. If a variable should not be included in any of the sets, set the corresponding
entry of set to NA. Using this specification of set it is not possible to
assign a variable to more than one sets. For such a case, set set to a list (as follows).
If set is a list, then each object in this list represents a set of variables. Therefore,
each object must be either a character or a numeric vector specifying either the names of the variables
that belongs to the respective set or the columns of data that contains these variables.
If names(set) is NULL, generic names will be employed as names for the sets. Otherwise,
names(set) are used. useN
logical specifying if the number of correctly classified out-of-bag observations should
be used in the computation of the importance measure. If FALSE, the proportion of
correctly classified oob observations is used instead. If NULL (default), then the
specification of useN in object is used.
iter
integer specifying the number of times the values of the variables in the respective set
are permuted in the computation of the importance of this set. If NULL (default), the values
of the variables are not permuted, but all variables belonging to the set are removed from the model
standardize
should a standardized version of the importance measure for a set of variables
be returned? By default, standardize = TRUE is used in the classification and the (multinomial)
logistic regression case, and standarize is set to FALSE in the linear regression case. For details, see mu.
mu
a non-negative numeric value. Ignored if standardize = FALSE. Otherwise, a t-statistic
for testing the null hypothesis that the importance of the respective set is equal to mu
is computed.
addMatImp
should the matrix containing the improvements due to each of the sets in each
of the logic regression models be added to the output?
prob.case
a numeric value between 0 and 1. If the logistic regression approach of logic
regression has been used in logic.bagging, then an observation will be classified as a case (or
more exactly, as 1), if the class probability of this observation is larger than prob.case.
Otherwise, prob.case is ignored.
rand
an integer for setting the random number generator in a reproducible state.