I am trying to running a multinomial logistic regression and am getting the error:
Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (1182) weights
My code is fairly simple and the model appears to build correctly if I just give the model a small subset of the data. The complete df has 743 obs of 908 variables. Is this just too much for the nnet program to handle?
xray <- read.csv("xray.csv", header = T, colClasses = "factor")
xray$out <- relevel(xray$Experiment, ref = "Hafted Axe")
yanke <- multinom(out ~., data = xray)
Many thanks