Hi everyone,
Can you help me to plot Gamma(x/h+1) and Beta(x/h+1,(1-x)/h+1)?I want write
x<-seq(0,3,0.1)
thank

2010/11/23 Neeti <nikkihathi@gmail.com>

>
> 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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>
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-- 
Francial Giscard LIBENGUE
Doctorant en Mathématiques Appliquées ;Option : Statistique
Université de Franche-Comté - UFR Sciences et Techniques
Laboratoire de Mathématiques de Besançon – UMR 6623 CNRS
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