Dear R-users I use nnet for a classification (2 classes) problem. I use the code CVnn1, CVnn2 as described in V&R. The thing I changed to the code is: I define the (class) weight for each observation in each cv 'bag' and give the vector of weights as parameter of nnet(..weights = weight.vector...) Unfortunately I get an error during some (but not all!) inner-fold cv runs: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ If you just remove the weights parameter in nnet() it runs fine!! I debugged the code but could not resolve the problem- it is really very strange and I need your help! I tried it very simple in defining the weights as = 1 for each obs (as it is by default)!: 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, ...) { resmatrix <- function(predict.matrix, learn, data, ri, i) { rae.matrix <- predict.matrix rae.matrix[,] <- 0 rae.vector <- as.numeric(as.factor((predict(learn, data[ri == i,], type = "class")))) for (k in 1:dim(rae.matrix)[1]) { if (rae.vector[k] == 1) rae.matrix[k,1] <- rae.matrix[k,1] + 1 else rae.matrix[k,2] <- rae.matrix[k,2] + 1 } rae.matrix } 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,]$GROUP weight.vector <- rep(1, dim(data[ri !=i,])[1]) for(rep in 1:nreps) { learn <- nnet(formula, data[ri !=i,], weights = weight.vector, trace = F, ...) #res[ri == i,] <- res[ri == i,] + predict(learn, data[ri == i,]) res[ri == i,] <- res[ri == i,] + resmatrix(res[ri == i,], learn, data, ri, i) } } if(verbose > 20) cat("\n") sum(as.numeric(truth) != max.col(res/nreps)) } truth <- data[,deparse(formula[[2]])] res <- matrix(0, nrow(data), length(levels(truth))) choice <- numeric(length(lambda)) for (i in sort(unique(rand))) { if(verbose > 0) cat("fold ", i,"\n", sep="") set.seed(i*i) ri <- sample(nifold, sum(rand!=i), replace=T) for(j in seq(along=lambda)) { if(verbose > 10) cat(" size =", size[j], "decay =", lambda[j], "\n") choice[j] <- CVnn1(formula, data[rand != i,], nreps=nreps, ri=ri, size=size[j], decay=lambda[j], verbose=verbose, ...) } decay <- lambda[which.is.max(-choice)] csize <- size[which.is.max(-choice)] if(verbose > 5) cat(" #errors:", choice, " ") # if(verbose > 1) cat("chosen size = ", csize, " decay = ", decay, "\n", sep="") for(rep in 1:nreps) { data.training <- data[rand != i,]$GROUP weight.vector <- rep(1, dim(data[rand !=i,])[1]) learn <- nnet(formula, data[rand != i,], weights = weight.vector, trace=F, size=csize, decay=decay, ...) #res[rand == i,] <- res[rand == i,] + predict(learn, data[rand == i,]) res[rand == i,] <- res[rand == i,] + resmatrix(res[rand == i,],learn,data, rand, i) } } factor(levels(truth)[max.col(res/nreps)], levels = levels(truth)) } res.nn2 <- CVnn2(GROUP ~ ., rae.data.subsetted1, skip = T, maxit = 500, nreps = cv.repeat) con(true = rae.data.subsetted$GROUP, predicted = res.nn2) ### 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 ######## Thanks a lot Best regards Christoph -- 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 lehmann at puk.unibe.ch CH-3000 Bern 60 puk.unibe.ch/cl/pn_ni_cv_cl_03.html