Usage
"logic.bagging"(x, y, B = 100, useN = TRUE, ntrees = 1, nleaves = 8, glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632, anneal.control = logreg.anneal.control(), oob = TRUE, onlyRemove = FALSE, prob.case = 0.5, importance = TRUE, addMatImp = FALSE, fast = FALSE, rand = NULL, ...)
"logic.bagging"(formula, data, recdom = TRUE, ...)
Arguments
x
a matrix consisting of 0's and 1's. Each column must correspond
to a binary variable and each row to an observation. Missing values are not allowed.
y
a numeric vector or a factor specifying the values of a response for all the observations
represented in x, where no missing values are allowed in y.
If a numeric vector, then y either contains
the class labels (coded by 0 and 1) or the values of a continuous response depending
on whether the classification or logistic regression approach of logic
regression, or the linear regression approach, respectively, should be used. If the response
is categorical, then y must be a factor naming the class labels of the observations.
B
an integer specifying the number of iterations.
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. Ignored if importance = FALSE.
ntrees
an integer indicating how many trees should be used.
For a binary response: If ntrees
is larger than 1, the logistic regression approach of logic regreesion
will be used. If ntrees is 1, then by default the classification
approach of logic regression will be used (see glm.if.1tree.)
For a continuous response: A linear regression model with ntrees trees
is fitted in each of the B iterations.
For a categorical response: $n.lev-1$ logic regression models with ntrees trees
are fitted, where $n.lev$ is the number of levels of the response (for details, see
mlogreg). nleaves
a numeric value specifying the maximum number of leaves used
in all trees combined. See the help page of the function logreg of
the package LogicReg for details.
glm.if.1tree
if ntrees is 1 and glm.if.1tree is TRUE
the logistic regression approach of logic regression is used instead of
the classification approach. Ignored if ntrees is not 1 or the response is not binary.
replace
should sampling of the cases be done with replacement? If
TRUE, a bootstrap sample of size length(cl) is drawn
from the length(cl) observations in each of the B iterations. If
FALSE, ceiling(sub.frac * length(cl)) of the observations
are drawn without replacement in each iteration.
sub.frac
a proportion specifying the fraction of the observations that
are used in each iteration to build a classification rule if replace = FALSE.
Ignored if replace = TRUE.
anneal.control
a list containing the parameters for simulated annealing.
See the help page of logreg.anneal.control in the LogicReg package.
oob
should the out-of-bag error rate (classification and logistic regression)
or the out-of-bag root mean square prediction error (linear regression), respectively, be computed?
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 outcome of the
logistic regression, i.e.\ the class probability, for an observation is
larger than prob.case, this observations will be classified as case
(or 1).
importance
should the measure of importance be computed?
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. fast
should a greedy search (as implemented in logreg) be used instead of simulated
annealing?
rand
numeric value. If specified, the random number generator will be
set into a reproducible state.
formula
an object of class formula describing the model that should be
fitted.
data
a data frame containing the variables in the model. Each row of data
must correspond to an observation, and each column to a binary variable (coded by 0 and 1)
or a factor (for details, see recdom) except for the column comprising
the response, where no missing values are allowed in data. The response must be either binary (coded by
0 and 1), categorical or continuous. If continuous, a linear model is fitted in each of the B iterations of
logicFS. If categorical, the column of data specifying the response must
be a factor. In this case, multinomial logic regressions are performed as implemented in mlogreg.
Otherwise, depending on ntrees (and glm.if.1tree)
the classification or the logistic regression approach of logic regression is used. recdom
a logical value or vector of length ncol(data) comprising whether a SNP should
be transformed into two binary dummy variables coding for a recessive and a dominant effect.
If recdom is TRUE (and a logical value), then all factors/variables with three levels will be coded by two dummy
variables as described in make.snp.dummy. Each level of each of the other factors
(also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable.
If recdom isFALSE (and a logical value),
each level of each factor is coded by an indicator variable. If recdom is a logical vector,
all factors corresponding to an entry in recdom that is TRUE are assumed to be SNPs
and transformed into two binary variables as described above. All variables corresponding
to entries of recdom that are TRUE (no matter whether recdom is a vector or a value)
must be coded either by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous),
and 3 (homozygous variant), or alternatively by the number of minor alleles, i.e. 0, 1, and 2, where
no mixing of the two coding schemes is allowed. Thus, it is not allowed that some SNPs are coded by
1, 2, and 3, and others are coded by 0, 1, and 2. ...
for the formula method, optional parameters to be passed to the low level function
logic.bagging.default. Otherwise, ignored.