[R] Regression with factor having1 level

[David Winsemius]

> 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
>> 
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> 
> -- 
> 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
> 
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> 

David Winsemius
Alameda, CA, USA