On 20 Nov 2015, at 10:07 , peter dalgaard <pda...@gmail.com> wrote: >> >> On 20 Nov 2015, at 04:53 , Damir Cosic <damir.co...@gmail.com> wrote: >> >> Hello, >> >> I am having problems with predict() after a multinomial logit regression by >> multinom(). I generate a design matrix with model.matrix() and use it to >> estimate the model. Then, if I pass the entire design matrix to predict(), >> it returns the same output as fitted(), which is expected. But if I pass >> only a few rows of the design matrix, it throws this error: >> >> Error in model.frame.default(Terms, newdata, na.action = na.omit, xlev >> = object$xlevels) : variable lengths differ (found for 'z') In addition: >> >> Warning message: 'newdata' had 6 rows but variables found have 15 rows > > Offhand (sorry, no time for testing things this morning) I suspect that you > are mixing paradigms. You can _either_ multiply coefficients with a design > matrix _or_ look up variables in a data frame, and I think you are trying to > look up variables in a matrix. In particular, I don't expect mm to have a > column called "z".
A little further thought later: The crux is that matrices will double as data frames in the newdata argument, but it will only work for numerical variables. Your model contains a numeric and a factor variable so it won't work, for two reasons: > head(mm) x ztall 1 -2.4963581 0 2 0.9895450 1 3 1.8755237 1 4 0.8911458 0 5 -2.1458457 1 6 0.6294571 1 I.e., there is no "z" column, but even if there were, it would mismatch with the model > colnames(mm)[2] <- "z" > p2<-predict(m,head(mm),"probs") Error: variable 'z' was fitted with type "factor" but type "numeric" was supplied In addition: Warning message: In model.frame.default(Terms, newdata, na.action = na.omit, xlev = object$xlevels) : variable 'z' is not a factor At this point, newdata=mm will fail too, as I predicted. (Pun almost unintended.) Notice that this works fine: > p2<-predict(m,head(df),"probs") > p2 good bad ugly 1 9.998923e-01 8.171733e-05 2.598999e-05 2 2.804424e-05 4.170214e-01 5.829506e-01 3 2.892377e-05 3.255878e-01 6.743832e-01 4 9.999315e-01 2.818836e-05 4.031584e-05 5 1.863116e-05 7.420142e-01 2.579672e-01 6 2.740359e-05 4.563101e-01 5.436625e-01 > -pd > > Accordingly, neither of your examples actually work, both cases find z (and > x?) in the global environment, it is just only in the latter example that the > inconsistency is discovered. I think you want either to use model.frame or an > explicit mm %*% coef(model) (or thereabouts). > > >> >> This is a minimal example: >> >> require(nnet) >> >> y<-factor(rep(c(1,2,3),5), levels=1:3, labels=c("good","bad","ugly")) >> x<-rnorm(15)+.2*rep(1:3,5) >> z<-factor(rep(c(1,2,2),5), levels=1:2, labels=c("short","tall")) >> >> df<-data.frame(y=y, x=x, z=z) >> mm<-model.matrix(~x+z, data=df)[,2:3] >> m<-multinom(y ~ x+z, data=df) >> >> p1<-predict(m,mm,"probs") >> >> p2<-predict(m,head(mm),"probs") >> >> My actual goal is out-of-sample prediction, but I could not make it work >> and, while debugging it, I reduced it to this problem. >> >> Best, >> >> Damir >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Email: pd....@cbs.dk Priv: pda...@gmail.com -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.