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# This program uses a Bootstrap method and calculates 95% percentile intervals of all the elements in a sample. The number of the original sample is n; resampling is repeated m times. class=c("c1","c2","c3","c4") # define all the elements probabilities=c(0.606557377,0.049180328,0.295081967,0.049180328) # and their probabilities n<-61 # the size of the orignal sample m<-50000 # the number of resample my.boot.c1<-numeric(0) # create a hash my.boot.c2<-numeric(0) my.boot.c3<-numeric(0) my.boot.c4<-numeric(0) for(i in 1:m) { y<-sample(class,n,replace=TRUE,prob=probabilities) #resampling my.boot.c1[i]<-length(grep("c1",y)) # count the number of elements, and put it in the hash my.boot.c2[i]<-length(grep("c2",y)) my.boot.c3[i]<-length(grep("c3",y)) my.boot.c4[i]<-length(grep("c4",y)) } Y<-matrix(nrow=4,ncol=3) # prepare a matrix to store results Y[1,1] <- mean(my.boot.c1) # encoding means in the first column Y[2,1] <- mean(my.boot.c2) Y[3,1] <- mean(my.boot.c3) Y[4,1] <- mean(my.boot.c4) Y[1,2-3]<-quantile(my.boot.c1,p=c(0.025,0.975)) # calculate the percentile intervals Y[2,2-3]<-quantile(my.boot.c2,p=c(0.025,0.975)) # and store them in Y Y[3,2-3]<-quantile(my.boot.c3,p=c(0.025,0.975)) Y[4,2-3]<-quantile(my.boot.c4,p=c(0.025,0.975)) Y # print # If you want each value mean<-Y[,1] # means lower<-Y[,2] # lower bound upper<-Y[,3] # upperbound