logic.bagging {logicFS} | R Documentation |
A first basic Bagging version of logic regression. Currently only the
classification and the logistic regression approach of logreg
are available.
## S3 method for class 'formula': logic.bagging(formula, data, ...) ## Default S3 method: logic.bagging(x, y, B = 100, ntrees = 1, nleaves = 8, glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632, anneal.control = logreg.anneal.control(), oob = TRUE, prob.case = 0.5, importance = TRUE, addMatImp = FALSE, rand = NULL, ...)
formula |
an object of class formula describing the model that should be
fitted |
data |
a data frame containing the variables in the model. Each column of data
must correspond to a binary variable (coded by 0 and 1), and each row to an observation |
x |
a matrix consisting of 0's and 1's. Each column must correspond to a binary variable and each row to an observation |
y |
a vector of 0's and 1's containing the class labels of the observations |
B |
an integer specifying the number of iterations |
ntrees |
an integer indicating how many trees should be used. 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 ) |
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 |
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 be computed? |
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.perm |
rand |
numeric value. If specified, the random number generator will be set into a reproducible state |
... |
for the formula method, optional parameters to be passed to the low level function
logic.bagging.default . Otherwise, ignored |
logic.bagging
returns an object of class logicBagg
containing
logreg.model |
a list containing the B logic regression models |
inbagg |
a list specifying the B Bootstrap samples |
vim |
an object of class logicFS (if importance = TRUE ) |
oob.error |
the out-of-bag error (if oob = TRUE ) |
... |
further parameters of the logic regression |
Holger Schwender, holger.schwender@udo.edu
Ruczinski, I., Kooperberg, C., LeBlanc M.L. (2003). Logic Regression. Journal of Computational and Graphical Statistics, 12, 475-511.
Schwender, H., Ickstadt, K. (2007). Identification of SNP Interactions Using Logic Regression. To appear in Biostatistics.
predict.logicBagg
, plot.logicBagg
,
logicFS
## Not run: # Load data. data(data.logicfs) # For logic regression and hence logic.bagging, the variables must # be binary. data.logicfs, however, contains categorical data # with realizations 1, 2 and 3. Such data can be transformed # into binary data by bin.snps<-make.snp.dummy(data.logicfs) # To speed up the search for the best logic regression models # only a small number of iterations is used in simulated annealing. my.anneal<-logreg.anneal.control(start=2,end=-2,iter=10000) # Bagged logic regression is then performed by bagg.out<-logic.bagging(bin.snps,cl.logicfs,B=20,nleaves=10, rand=123,anneal.control=my.anneal) # The output of logic.bagging can be printed bagg.out # By default, also the importances of the interactions are # computed bagg.out$vim # and can be plotted. plot(bagg.out) # The original variable names are displayed in plot(bagg.out,coded=FALSE) # New observations (here we assume that these observations are # in data.logicfs) are assigned to one of the classes by predict(bagg.out,data.logicfs) ## End(Not run)