cv.fold<-function(i, size=3, rang=0.3){
cat('Fold ', i, '\n')
out.fold.c <-((i-1)*c.each.part +1):(i*c.each.part)
out.fold.n <-((i-1)*n.each.part +1):(i*n.each.part)
train.cv <- n.cc[-out.fold.c, c(2:2401, 2417)]
train.nv <- n.nn[-out.fold.n, c(2:2401, 2417)]
train.v<-rbind(train.cv, train.nv) #training data for feature
selection
# grow tree
fit.dimer <- rpart(as.factor(out) ~ ., method="class", data=train.v)
at<-grep("<leaf>", fit.dimer$frame[, "var"], value=FALSE, ignore.case=TRUE)
varr<-as.character(unique(fit.dimer$frame[-at, "var"]))
train.cc <- n.cc[-out.fold.c,]
valid.cc <- n.cc[out.fold.c,]
train.nn <- n.nn[-out.fold.n,]
valid.nn <- n.nn[out.fold.n,]
train<-rbind(train.cc, train.nn) #training data
valid<-rbind(valid.cc, valid.nn) # validation data
#creat data set contains the following variables
myvar<-names(gh9_h) %in% c(varr, "out")
train<-train[myvar] # update training set
valid<-valid[myvar]
nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=size, rang=rang,
decay=5e-4, maxit=500) # model fitting
#get the validation error
mc<-table(valid$out, predict(nnet.fit, valid, type="class")) #confusion
matrix
fp<-mc[1,2]/sum(mc[1,]) #false positive
fn<- mc[2,1]/sum(mc[2,]) #false negative
accuracy.r<-1-(mc[1,2]+mc[2,1])/sum(mc) #total accuracy rate
return(c(fp, fn, accuracy.r))
}
result.fun <- lapply(1:2, cv.fold(i, size=5, rang=0.3))
I got the following error message:
*Error in match.fun(FUN) :
'cv.fold(i, size = 5, rang = 0.3)' is not a function, character or symbol
I do want to change the size and rang parameters some time.
*
Can anyone help me this this? Thanks so much!
--
Sincerely,
Changbin
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