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## SIMCA missclassification
## Computing missclassification rates and confusion matrix for
## SIMCA is available, but not very convenient and not documented.
##
## If you call predict without 'newdata', resubstitution will be
## performed and the apparent error rate and confusion table will be printed.
##
## If you have new data and you know the group membership,
## you can compute missclassification error and confusion
## table as shown in the example below.
##
library(rrcovHD)
data(pottery)
dim(pottery) # 27 observations in 2 classes, 6 variables
head(pottery)
## Build the SIMCA model. Use RSimca for a robust version
cs <- CSimca(origin~., data=pottery)
cs
summary(cs)
predict(cs) ## without new data - perform resubstitution
## generate a sample from the pottery data set -
## this will be the "new" data to be predicted
smpl <- sample(1:nrow(pottery), 5)
test <- pottery[smpl, -7] # extract the test sample. Remove the last (grouping) variable
grp <- pottery[smpl, 7] # remember the grouping variable also
print(test)
## predict new data
pr <- predict(cs, newdata=test)
pr@classification
ctab <- rrcov::mtxconfusion(grp, pr@classification)
ctab
acctab <- t(apply(ctab, 1, function(x) x/sum(x)))
dimnames(acctab) <- dimnames(ctab)
acctab
miss <- 1 - sum(diag(ctab))/sum(ctab)
miss
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