[R] linear hypothesis test in gls model
jfox at mcmaster.ca
Sat Jun 16 19:21:55 CEST 2007
linear.hypothesis() currently has no method specifically for gls objects,
and so this usage invokes the default method. I'm not sure off-hand what's
appropriate for an F-test in this context (and indeed why the default test
is inappropriate). Can you describe the correct test or supply a reference?
I suspect that it shouldn't be hard to write a linear.hypothesis method for
gls objects that fixes up the result returned by linear.hypothesis.default.
You might take a look at car:::linear.hypothesis.default to see that it does
-- the computations are pretty straightforward.
I hope this helps,
John Fox, Professor
Department of Sociology
Canada L8S 4M4
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Tine Huyghe
> Sent: Saturday, June 16, 2007 4:19 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] linear hypothesis test in gls model
> Dear all,
> For analysis of a longitudinal data set with fixed
> measurement in time I built a gls model (nlme). For testing
> hypotheses in this model I used the linear.hypothesis
> function from the car package. A check with the results
> obtained in SAS proc MIXED with a repeated statement revealed
> an inconsistency in the results. The problem can be that the
> linear.hypothesis function (1) only gives the asymptotic chi
> square test and/or (2) only uses the residual error. Is there
> another solution to testing linear hypotheses in a gls model?
> Thanks in advance
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