for loop error "attempt to select less than one element" in R

Question

I have created the correct number of indeces for my vectors and I am trying to input the i'th element from the for loop as the index to hold the classification error value. But I get the error:

Error in indeces.gen_error[[i]] <- paste(classification_error) :
attempt to select less than one element

uspscl.txt

uspsdata.txt

My code:

library(e1071)
library(caret)

set.seed(733)
uspscldf = read.table('uspscl.txt', header=F, sep=',')
uspsdatadf = read.table('uspsdata.txt', header=F, sep='\t')

trainIndex <- createDataPartition(uspscldf$V1,list=FALSE, p = .80,times=1)
dataTrain <- uspsdatadf[ trainIndex,]
dataTest  <- uspsdatadf[-trainIndex,]

classTrain <- uspscldf[ trainIndex,]
classTest  <- uspscldf[-trainIndex,]

indeces = seq(0.00001, 1, by=0.001)

indeces.gen_error = NULL
indeces.softmargin = NULL
for (i in seq(0.00001, 1, by=0.001)){
    # For svm(): soft margin is "cost" 
    # Gaussian kernel bandwidth (sigma) = is implicitly defined by "gamma"
    # kernal=radial is non-linear while kernal=linear is linear
    svm.model <- svm(classTrain ~ ., data = dataTrain, cost = i,type="C-classification",kernal = "linear")

    svm.pred <- predict(svm.model, dataTrain)

    # confusion matrix
    tab <- table(pred = svm.pred, true = classTrain)

    classification_error <- 1- sum(svm.pred == classTrain)/length(svm.pred)

    indeces.gen_error[[i]] <- paste(classification_error)
    indeces.softmargin[[i]]<-i
}

I printed the first i in the first iteration and it give 1e-5 which is correct so I am at a loss as to why it says I am selecting less than one element. Any help would be appreciated. Thanks

ANSWER::: I did not see Pierre's answer to this until I solved the answer myself but his explanation is better so I am accepting his answer. My new code now is:

indeces = seq(0.00001, 1, by=0.001)

indeces.gen_error = NULL
indeces.softmargin = NULL
count=0
for (i in indeces){
  count=count+1
    # For svm(): soft margin is "cost" 
    # Gaussian kernel bandwidth (sigma) = is implicitly defined by "gamma"
    # kernal=radial is non-linear while kernal=linear is linear
    svm.model <- svm(classTrain ~ ., data = dataTrain, cost = i,type="C-classification",kernal = "linear")

    svm.pred <- predict(svm.model, dataTrain)

    # confusion matrix
    tab <- table(pred = svm.pred, true = classTrain)

    classification_error <- 1- sum(svm.pred == classTrain)/length(svm.pred)

    indeces.gen_error[[count]] <- paste(classification_error)
    indeces.softmargin[[count]]<-i
}

Answer 1

#Example
x <- NULL
for( i in seq(0.01, 1, .01)) {
  a <- 10 * i
  x[[i]] <- paste("b", a)
}
# Error in x[[i]] <- paste("b", a) : 
#   attempt to select less than one element

#The right way
x <- NULL
myseq <- seq(0.01, 1, 0.01)
for( i in 1:length(myseq)) {
  a <- 10 * myseq[i]
  x[i] <- paste("b", a)
}

Why the first way fails for( i in seq(0.01, 1, .01)) will use the sequence as 'i'. Anytime when a loop fails, the first way to troubleshoot is to try out each loop one by one. So each loop takes a value of the sequence and enters it wherever there is an i. The first loop looks like:

for (i in seq(0.00001, 1, by=0.001)){

    svm.model <- svm(classTrain ~ ., data = dataTrain, cost = 0.00001,type="C-classification",kernal = "linear")

    svm.pred <- predict(svm.model, dataTrain)

    # confusion matrix
    tab <- table(pred = svm.pred, true = classTrain)

    classification_error <- 1- sum(svm.pred == classTrain)/length(svm.pred)

    indeces.gen_error[[0.00001]] <- paste(classification_error)
    indeces.softmargin[[0.00001]]<- 0.00001
}

Do you see the problem? With indeces.gen_error[[0.00001]] pay attention to what is happening here. You did not mean to do this. You meant for indeces.gen_error[[1]] to be the first entry.

You are subsetting by a decimal. If we had:

x <- 1:10

What do you think would happen with x[2.5]? We are asking R for the element at the position 2.5. That doesn't make sense. There is no half position. There's either the 2nd or 3rd. Try it to see what is returned.

In your loop you are asking R for indeces.gen_error[[0.00001]]. Therefore, you are requesting the 1/100,000th position. That doesn't make sense. The evaluator will force the subset to be an integer. It goes to 0. And we get an error.