Dear R-users, dear Prof. Ripley as package maintainer
I tried to investigate the odd error, when I call nnet together with a 'weights' parameter, using the 'fgl' example in V&R p 348
The error I get is:
Error in eval(expr, envir, enclos) : Object "w" not found
I think it is a kind of scoping problem, but I really cannot see, what the problem exactly is.
and here is my code: the only thing which changed is the definition of a weight-parameter ('w') which is given to the nnet-call. Of course the weight vector with '1's makes no sense here, but it will be defined according to the class sizes later.
### library(MASS) data(flg)
con <- function(...)
{
print(tab <- table(...))
diag(tab) <- 0
cat("error rate = ",
round(100*sum(tab)/length(list(...)[[1]]), 2), "%\n")
invisible()
}
set.seed(123) rand <- sample(10, dim(fgl)[1], replace = T)
fgl1 <- fgl
fgl1[1:9] <- lapply(fgl[, 1:9], function(x) {r <- range(x); (x - r[1])/diff(r)})
CVnn2 <- function(formula, data,
size = c(0,4,4,10,10), lambda = c(0, rep(c(0.001, 0.01),2)),
nreps = 1, nifold = 5, verbose = 99, ...)
{
CVnn1 <- function(formula, data, nreps=1, ri, verbose, ...)
{
totalerror <- 0
truth <- data[,deparse(formula[[2]])]
res <- matrix(0, nrow(data), length(levels(truth)))
if(verbose > 20) cat(" inner fold")
for (i in sort(unique(ri))) {
if(verbose > 20) cat(" ", i, sep="")
data.training <- data[ri != i,]$GROUPw <- rep(1, dim(data[ri !=i,])[1])
for(rep in 1:nreps) {
learn <- nnet(formula, data[ri !=i,],
weights = w,
trace = F, ...)
res[ri == i,] <- res[ri == i,] + predict(learn, data[ri == i,])
}
}
if(verbose > 20) cat("\n")
sum(as.numeric(truth) != max.col(res/nreps))
}
truth <- data[,deparse(formula[[2]])]
res <- matrix(0, nrow(data), length(levels(truth)))
choice <- numeric(length(lambda))
for (i in sort(unique(rand))) {
if(verbose > 0) cat("fold ", i,"\n", sep="")
set.seed(i*i)
ri <- sample(nifold, sum(rand!=i), replace=T)
for(j in seq(along=lambda)) {
if(verbose > 10)
cat(" size =", size[j], "decay =", lambda[j], "\n")
choice[j] <- CVnn1(formula, data[rand != i,], nreps=nreps,
ri=ri, size=size[j], decay=lambda[j],
verbose=verbose, ...)
}
decay <- lambda[which.is.max(-choice)]
csize <- size[which.is.max(-choice)]
if(verbose > 5) cat(" #errors:", choice, " ") #
if(verbose > 1) cat("chosen size = ", csize,
" decay = ", decay, "\n", sep="")
for(rep in 1:nreps) {
data.training <- data[rand != i,]$GROUP
w <- rep(1, dim(data[rand !=i,])[1])
learn <- nnet(formula, data[rand != i,],
weights = w,
trace=F,
size=csize, decay=decay, ...)
res[rand == i,] <- res[rand == i,] + predict(learn, data[rand == i,])
}
}
factor(levels(truth)[max.col(res/nreps)], levels = levels(truth))
}
res.nn2 <- CVnn2(type ~ ., fgl1, skip = T, maxit = 500, nreps = 10)
con(true = fgl$type, predicted = res.nn2) ##
many thanks for your help
Christoph
###
Coordinates: platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 9.1 year 2004 month 06 day 21 language R
-- Christoph Lehmann Phone: ++41 31 930 93 83 Department of Psychiatric Neurophysiology Mobile: ++41 76 570 28 00 University Hospital of Clinical Psychiatry Fax: ++41 31 930 99 61 Waldau [EMAIL PROTECTED] CH-3000 Bern 60 http://www.puk.unibe.ch/cl/pn_ni_cv_cl_03.html
______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
