I can't reproduce this example: I get
smalltest <- nnet.formula(formula = Q65 ~ ., data = smallx,
+ size = 2, rang = 1, maxit = 250, subset = jj)
Error in get(ctr)(levels(x), contrasts = contrasts) :
Contrasts not defined for 0 degrees of freedom
And that's correct: what you are doing is nonsense. You've included
one-level factors, and you are fitting a non-linear model to a linearly
over-determined system (there are more X columns than data points).
I don't know how you got this to work, but I would not be surprised
if predict.nnet failed on an over-determined fit: It's not designed for
misuse.
I don't agree this is a bug in my code.
On Mon, 26 Nov 2001 jcs20@po.cwru.edu wrote:
Full_Name: Jeff Schwarz Version: R1.3.1 OS: Windows 2000 Submission from: (NULL) (129.22.170.115) Error message (using predict and predict.nnet)
predict (smalltest, smallx[-jj,])
Error in matrix(NA, length(keep), nout, dimnames = list(rn,
dimnames(object$fitted)[[2]])) :
length of dimnames[1] not equal to array extent
*** all relevant code and data source is given below ***
I have successfully built an object of type nnet using...
nnet.formula(formula = Q65 ~ ., data = smallx, size = 2, rang = 1,
maxit = 250, subset = jj)
where smallx is a data.frame with dimensions 300 by 103 that contains numerical
and factor variables. Smallx is a random sample of 300 from the tab-delimited
numeric dataset found at
http://www.gvu.gatech.edu/user_surveys/survey-1998-10/datasets/num_general.zip.
The code I used to recode the data is below, also.
I have successfully run the sample code from the predict.nnet help file without
any problem.
*********************************************************************
data source:
http://www.gvu.gatech.edu/user_surveys/survey-1998-10/datasets/num_general.zip
#***Get the data from the big file
#***Get rid of the vairiables for survey (all
#*** observations = general), a unique identifier
#*** for each respondent and an extra variable X
#*** (all = NaN)
#***Make sure that all the asymmetric binary,
#*** and nominal variables are coded as factor,
#*** not numeric (daisy has special methods for
#*** symmetric binary and discrete ordinal)
#***Recode some of the discrete ordinal variables
#*** so the Na, didn't know, and didn't say are all
#*** "NaN", and so that a few of the "never" responses
#*** are coded as 0 instead of 5
bigx <-
read.delim (file location,
header = TRUE, sep = "\t", quote = "\"", dec = ".",
fill = TRUE)
bigx <- bigx [,c(1:103)]
ii <- bigx[,c(22)] == 5
bigx[,c(22)] <- replace (bigx[,c(22)], ii, 0)
ii <- bigx[,c(23)] == 5
bigx[,c(23)] <- replace (bigx[,c(23)], ii, 0)
ii <- bigx[,c(24)] == 5
bigx[,c(24)] <- replace (bigx[,c(24)], ii, 0)
ii <- bigx[,c(25)] == 5
bigx[,c(25)] <- replace (bigx[,c(25)], ii, 0)
ii <- bigx[,c(26)] == 5
bigx[,c(26)] <- replace (bigx[,c(26)], ii, 0)
ii <- bigx[,38] == 7
bigx[,38] <- replace (bigx[,38], ii, NaN)
ii <- bigx[,38] == 8
bigx[,38] <- replace (bigx[,38], ii, NaN)
ii <- bigx[,57] == 1
bigx[,57] <- replace (bigx[,57], ii, NaN)
ii <- bigx[,58] == 19
bigx[,58] <- replace (bigx[,58], ii, NaN)
ii <- bigx[,60] == 4
bigx[,60] <- replace (bigx[,60], ii, NaN)
ii <- bigx[,60] == 1
bigx[,60] <- replace (bigx[,60], ii, 3)
ii <- bigx[,60] == 2
bigx[,60] <- replace (bigx[,60], ii, 1)
ii <- bigx[,60] == 3
bigx[,60] <- replace (bigx[,60], ii, 2)
ii <- bigx[,69] == 1
bigx[,69] <- replace (bigx[,69], ii, NaN)
ii <- bigx[,69] == 7
bigx[,69] <- replace (bigx[,69], ii, NaN)
bigx[,69] <- bigx[,69] - 1
for (ii in c(1:9,14:21,27:32,34:37,39:49,52:55,
59,61:70,72:103)) bigx[,ii] <- as.factor(bigx[,ii])
library (nnet)
set.seed(7261)
smallx <- bigx[sample(1:5022,300),]
jj <- sample (1:300, 100)
smalltest <- nnet.formula(formula = Q65 ~ ., data = smallx,
size = 2, rang = 1, maxit = 250, subset = jj)
predict.nnet (smalltest, smallx[-jj,])
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