Arguments
log.out
an object of class logicBagg, i.e.\ the output of
logic.bagging.
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.
onlyRemove
should in the single tree case the multiple tree measure be used? If TRUE,
the prime implicants are only removed from the trees when determining the importance in the
single tree case. If FALSE, the original single tree measure is computed for each prime
implicant, i.e.\ a prime implicant is not only removed from the trees in which it is contained,
but also added to the trees that do not contain this interaction. Ignored in all other than the
classification case.
prob.case
a numeric value between 0 and 1. If the logistic regression approach
of logic regression is used (i.e.\ if the response is binary, and in logic.bagging
ntrees is set to a value larger than 1, or glm.if.1tree is
set to TRUE), then an observation will be classified as a case (or
more exactly as 1), if the class probability of this observation estimated
by the logic bagging model is larger than prob.case.
addInfo
should further information on the logic regression models
be added?
addMatImp
should the matrix containing the improvements due to the prime implicants
in each of the iterations be added to the output? (For each of the prime implicants,
the importance is computed by the average over the B improvements.) Must be
set to TRUE, if standardized importances should be computed using
vim.norm, or if permutation based importances should be computed
using vim.signperm.