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Dear all, I sample without replacement elements of a vector and generate a new vector: kl<-c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,5,5,5,5,5,6,6,6,6,6,7,7,8,8, 8,8,8,8,8,8,8) the_index<-c(sample(40,35)) for(fs in 1:length(the_index)){if(fs==1){s<-c(kl[the_index[fs]])}else{s<- append(s, kl[the_index[fs]], after = length(s))}} I am running in BATCH mode this script in a

Dear all, I am trying to construct a wrapper that uses random forest to evaluate the subsets using RWeka and when I do: nombi <- make_Weka_filter("weka/attributeSelection/WrapperSubsetEval") datbin<- nombi(gene ~., data=X1X2X3X4W, control =Weka_control( B = list("weka.classifiers.trees.RandomForest"))) I also have tried with an other induction algorithm:

Hi, Does anyone know what the negative indexing of a matrix mean? I am using the RWeka and this evaluate classifier does not work on new data like this e <- evaluate_Weka_classifier(m1235,newdata=XW4, complexity = FALSE,class = FALSE) while it work with negative indexing: e <- evaluate_Weka_classifier(m1235,newdata=XW4[,-2], complexity = FALSE,class = FALSE) Although I

Hi, Does anyone know what the negative indexing of a matrix mean? I am using the RWeka and this evaluate classifier does not work on new data like this e <- evaluate_Weka_classifier(m1235,newdata=XW4, complexity = FALSE,class = FALSE) while it work with negative indexing: e <- evaluate_Weka_classifier(m1235,newdata=XW4[,-2], complexity = FALSE,class = FALSE) Although I

Dear All, I am finding trouble trying to guild a Wrapper using random forest to evaluate the subsets: I do: nombi <- make_Weka_filter("weka/filters/supervised/attribute/AttributeSelection") datbin<- nombi(gene ~., data=X1X2X4X5W, control =Weka_control( S=list("weka.attributeSelection.GeneticSearch"), E=list("weka.attributeSelection.WrapperSubsetEval"),B