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.
logic.bagging(data, cl, B = 100, ntrees = 1, nleaves = 8, glm.if.1tree = FALSE, anneal.control = logreg.anneal.control(), oob = TRUE, prob.case = 0.5, importance = TRUE, rand = NULL)
data |
a matrix consisting of 0's and 1's. Each column must correspond to a binary variable and each row to an observation. |
cl |
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. |
anneal.control |
a list containing the parameters for simulated annealing.
See ?logreg.anneal.control of 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 predicted 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? |
rand |
numeric value. If specified, the random number generator will be set into a reproducible state. |
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 |
Tech. Report on the feature selection using logic regression will be available soon.
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.
predict.logicBagg
, plot.logicBagg
,
logic.fs
## 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)