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#' predict projection pursuit classification tree
#'
#' Predict class for the test set with the fitted projection pursuit
#' classification tree and calculate prediction error.
#' @title predict PPtree
#' @param object a fitted object of class inheriting from "PP.Tree.class"
#' @param newdata the test dataset
#' @param Rule split rule 1: mean of two group means
#' 2: weighted mean of two group means
#' - weight with group size
#' 3: weighted mean of two group means
#' - weight with group sd
#' 4: weighted mean of two group means
#' - weight with group se
#' 5: mean of two group medians
#' 6: weighted mean of two group medians
#' - weight with group size
#' 7: weighted mean of two group median
#' - weight with group IQR
#' 8: weighted mean of two group median
#' - weight with group IQR and size
#' @param ... arguments to be passed to methods
#' @aliases predict
#' @references Lee, YD, Cook, D., Park JW, and Lee, EK(2013)
#' PPtree: Projection Pursuit Classification Tree,
#' Electronic Journal of Statistics, 7:1369-1386.
#' @export
#' @keywords tree
#' @examples
#' data(iris)
#' n <- nrow(iris)
#' tot <- c(1:n)
#' n.train <- round(n*0.9)
#' train <- sample(tot,n.train)
#' test <- tot[-train]
#' Tree.result <- PPTreeclass(Species~., data=iris[train,],"LDA")
#' predict(Tree.result)
#' @import stats
predict.PPtreeclass<-function(object,newdata=NULL,Rule=1,...) {
Tree.result<-object
if(is.null(newdata))
newdata<-Tree.result$origdata
test.data<-as.matrix(newdata)
PP.Classification<-function(Tree.Struct,test.class.index,IOindex,
test.class,id,rep){
if(Tree.Struct[id,4]==0){
i.class<-test.class
i.class[i.class>0]<-1
i.class<-1-i.class
test.class<-test.class+IOindex*i.class*Tree.Struct[id, 3]
return(list(test.class=test.class,rep=rep))
} else{
IOindexL<-IOindex*test.class.index[rep,]
IOindexR<-IOindex*(1-test.class.index[rep,])
rep<-rep+1
a<-PP.Classification(Tree.Struct,test.class.index,IOindexL,
test.class,Tree.Struct[id,2],rep)
test.class<-a$test.class
rep<-a$rep;
a<-PP.Classification(Tree.Struct,test.class.index,IOindexR,
test.class,Tree.Struct[id,3],rep)
test.class<-a$test.class
rep<-a$rep
}
list(test.class=test.class,rep=rep)
}
PP.Class.index<-function(class.temp,test.class.index,test.data,
Tree.Struct,Alpha.Keep,C.Keep,id,Rule){
class.temp<-as.integer(class.temp)
if(Tree.Struct[id,2]==0){
return(list(test.class.index=test.class.index,class.temp=class.temp))
} else{
t.class<-class.temp
t.n<-length(t.class[t.class==0])
t.index<-sort.list(t.class)
if(t.n)
t.index<-sort(t.index[-(1:t.n)])
t.data<-test.data[t.index,]
id.proj<-Tree.Struct[id,4]
proj.test<-as.matrix(test.data)%*%as.matrix(Alpha.Keep[id.proj,])
proj.test<-as.double(proj.test)
class.temp<-t(proj.test<C.Keep[id.proj,Rule])
test.class.index<-rbind(test.class.index,class.temp)
a<-PP.Class.index(class.temp,test.class.index,test.data,
Tree.Struct,Alpha.Keep,C.Keep,
Tree.Struct[id,2],Rule)
test.class.index<-a$test.class.index
a<-PP.Class.index(1-class.temp,test.class.index,test.data,
Tree.Struct,Alpha.Keep,C.Keep,
Tree.Struct[id,3],Rule)
test.class.index<-a$test.class.index;
}
list(test.class.index=test.class.index,class.temp=class.temp)
}
n<-nrow(test.data)
class.temp<-rep(1,n)
test.class.index<-NULL
temp<-PP.Class.index(class.temp,test.class.index,test.data,
Tree.result$Tree.Struct,Tree.result$projbest.node,
Tree.result$splitCutoff.node,1,Rule)
test.class<-rep(0,n)
IOindex<-rep(1,n)
temp<-PP.Classification(Tree.result$Tree.Struct,temp$test.class.index,
IOindex,test.class,1,1)
class.name<-names(table(Tree.result$origclass))
predict.class<-factor(class.name[temp$test.class])
return(predict.class)
}
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