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2011 Jan 05
0
Nnet and AIC: selection of a parsimonious parameterisation
...209) cpus2 <- cpus[, 2:8] # excludes names, authors? predictions attach(cpus2) cpus3 <- data.frame(syct = syct-2, mmin = mmin-3, mmax = mmax-4, cach=cach/256,chmin=chmin/100, chmax=chmax/100, perf) detach() CVnn.cpus <- function(formula, data = cpus3[cpus.samp, ], maxSize = 10, decayRange = c(0,0.2), nreps = 5, nifold = 10, alpha= 9/10, linout = TRUE, skip = TRUE, maxit = 1000,...){ #nreps=number of attempts to fit a nnet model with randomly chosen initial parameters # The one with the smallest RSS on the training data is then chosen nnWtsPrunning <-function(nn,data,...