Regression with factor having1 level
On Mar 11, 2016, at 12:48 AM, peter dalgaard <pdalgd at gmail.com> wrote:
On 11 Mar 2016, at 08:25 , David Winsemius <dwinsemius at comcast.net> wrote:
...
dfrm <- data.frame(y=rnorm(10), x1=rnorm(10) ,x2=as.factor(TRUE), x3=rnorm(10)) lm(y~x1+x2+x3, dfrm, na.action=na.exclude)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied
Yes, and the error appears to come from `model.matrix`:
model.matrix(y~x1+factor(x2)+x3, dfrm)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels
Actually not. The above is because you use an explicit factor(x2). The actual smoking gun is this line in lm() mf$drop.unused.levels <- TRUE
It's possible that modifying model.matrix to allow single level factors would then bump up against that check, but at the moment the traceback() from an error generated with data that has a single level factor and no call to factor in the formula still implicates code in model.matrix:
dfrm <- data.frame(y=rnorm(10), x1=rnorm(10) ,x2=factor(TRUE), x3=rnorm(10)) lm(y~x1+x2+x3, dfrm)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels
traceback()
5: stop("contrasts can be applied only to factors with 2 or more levels")
4: `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]])
3: model.matrix.default(mt, mf, contrasts)
2: model.matrix(mt, mf, contrasts)
1: lm(y ~ x1 + x2 + x3, dfrm)
David. > > which someone must have thought was a good idea at some point.... > > model.matrix itself is quite happy to leave factors alone and let subsequent code sort out any singularities, e.g. > >> model.matrix(y~x1+x2, data=df[1:2,]) > (Intercept) x1 x2B > 1 1 1 0 > 2 1 1 0 > attr(,"assign") > [1] 0 1 2 > attr(,"contrasts") > attr(,"contrasts")$x2 > [1] "contr.treatment" > > > >>> model.matrix(y~x1+x2+x3, dfrm) >> (Intercept) x1 x2TRUE x3 >> 1 1 0.04887847 1 -0.4199628 >> 2 1 -1.04786688 1 1.3947923 >> 3 1 -0.34896007 1 -2.1873666 >> 4 1 -0.08866061 1 0.1204129 >> 5 1 -0.41111366 1 -1.6631057 >> 6 1 -0.83449110 1 1.1631801 >> 7 1 -0.67887823 1 0.3207544 >> 8 1 -1.12206068 1 0.6012040 >> 9 1 0.05116683 1 0.3598696 >> 10 1 1.74413583 1 0.3608478 >> attr(,"assign") >> [1] 0 1 2 3 >> attr(,"contrasts") >> attr(,"contrasts")$x2 >> [1] "contr.treatment" >> >> -- >> >> David Winsemius >> Alameda, CA, USA >> >> ______________________________________________ >> R-help at 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 > Office: A 4.23 > Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com > > > > > > > > > David Winsemius Alameda, CA, USA