[R] cross validation using e1071:SVM

[Neeti]

could anyone help me with my last problem. if the question is not clear
please let me know

thank you  

Hi everyone 
 
 I am trying to do cross validation (10 fold CV) by using e1071:svm method. 
 I 
 know that there is an option (“cross”) for cross validation but still I 
 wanted to make a function to Generate cross-validation indices  using pls: 
 cvsegments method. 
 
 ##################################################################### 

 Code (at the end) Is working fine but sometime caret:confusionMatrix gives
 following error: 

 stat_result<- confusionMatrix(pred_true1,species_test) 

 Error in confusionMatrix.default(pred_true1, species_test) : 
  The data and reference factors must have the same number of levels 

 My data: total number=260 
        Class = 6 

 ##################################### 
 Sorry if I missed some previous discussion about this problem. 

 It would be nice if anyone explain or point out the mistake I am doing in
 this following code. 

 Is there another way to do this? As I wanted to check my result based on
 Accuracy and Kappa value generated by caret:confusionMatrix. 

 ########################################## 
 Code 
 ######################################### 
 x<-NULL 
 index<-cvsegments(nrow(data),10) 
 for(i in 1:length(index)) 
 { 
        x<-matrix(index[i]) 
        testset<-data[x[[1]],] 
        trainset<-data[-x[[1]],] 

        species<-as.factor(trainset[,ncol(trainset)]) 
        train1<-trainset[,-ncol(trainset)] 
        train1<-train1[,-(1)] 

        test_t<-testset[,-ncol(testset)] 
        species_test<-as.factor(testset[,ncol(testset)]) 
        test_t<-test_t[,-(1)] 
        model_true1 <- svm(train1,species) 
        pred_true1<-predict(model_true1,test_t) 
        stat_result<- confusionMatrix(pred_true1,species_test) 
        stat_true[[i]]<-as.matrix(stat_result,what="overall") 
        kappa_true[i]<-stat_true[[i]][2,1] 
        accuracy_true[i]<-stat_true[[i]][1,1] 
 }

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