logic.bagging {logicFS}R Documentation

Bagged Logic Regression

Description

A first basic Bagging version of logic regression. Currently only the classification and the logistic regression approach of logreg are available.

Usage

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)

Arguments

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.

Value

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

Note

Tech. Report on the feature selection using logic regression will be available soon.

Author(s)

Holger Schwender, holger.schwender@udo.edu

References

Ruczinski, I., Kooperberg, C., LeBlanc M.L. (2003). Logic Regression. Journal of Computational and Graphical Statistics, 12, 475-511.

See Also

predict.logicBagg, plot.logicBagg, logic.fs

Examples

## 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)

[Package logicFS version 1.2.0 Index]