[R] Regression with factor having1 level

[peter dalgaard]

> On 11 Mar 2016, at 17:56 , David Winsemius <dwinsemius at comcast.net> wrote:
> 
>> 
>> 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:

You're missing the point: model.matrix has a beef with 1-level factors, not with 2-level factors of which one level happens to be absent, which is what this thread was originally about. It is lm that via model.frame with drop.unused.levels=TRUE converts the latter factors to the former.

-pd 


> 
>> 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
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
> 
> David Winsemius
> Alameda, CA, USA

-- 
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