Hi,
Ive written some code to obtain a confusion matrix when the true
classification and the predicted classification are known. Suppose true
classification is called tr and predicted classification is pr. I have 4
classes in tr, but only 3 classes out of 4 are predicted in pr. Following is
my code, but looks quite clunky to me. I wonder if you have any suggestions
to improve it.
Thanks,
Monica
-----------------------------
tr <- c(1,2,2,3,3,3,2,4,1,1)
pr<-c(1,2,1,3,3,3,1,2,1,2)
dat <- data.frame(tr, pr)
class <- c(1:length(tr))
m <- max(c(length(unique(tr)), length(unique(pr))))
for(i in 1:length(class)) {
class[i] <- sub(' ','',paste(dat[i,1],dat[i,2])) }
dat <- data.frame(dat, class)
mat <- matrix(0, nrow=m, ncol=m)
for (i in 1:m){
for (j in 1:m){
mat[i,j] <- sub(' ','',paste(i,j))
}}
cat <- matrix(0, nrow=(m+1), ncol=(m+1))
for (i in 1:m){
for(j in 1:m){
cat[i,j]<- nrow(dat[dat$class==mat[i,j],])
}}
for (i in 1:m){
cat[(m+1),i]<-sum(cat[1:m,i])
cat[i,(m+1)]<- sum(cat[i,1:m])
cat[(m+1),(m+1)] <- sum(cat[1:m,(m+1)])
}
cat
[,1] [,2] [,3] [,4] [,5]
[1,] 2 1 0 0 3
[2,] 2 1 0 0 3
[3,] 0 0 3 0 3
[4,] 0 1 0 0 1
[5,] 4 3 3 0 10
The 5th row / col represents the sum on each row / col respectively.
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