[R] proc mixed vs. lme

[Douglas Bates]

"Grathwohl,Dominik,LAUSANNE,NRC/NT" <dominik.grathwohl at rdls.nestle.com> writes:

> Hallo Peter,
> 
> Thank you for the advice, now I have to update my table:
> 
>                             SAS                           R
> random statement            random subj(program);         random = ~ 1 |
> Subj
> -2*loglik                   1420.8                        1420.820
> random effects
> variance(Intercept)         9.6033                        9.603331
> variance(residual)          1.1969                        1.196873
> the first 3 fixed effects
> intercept                   83.0952                        83.09524
> ProgramCont                -3.4952                       -3.49524
> ProgramRI                  -1.9702                       -1.97024
> ...                        ...                            ...
> 
> Everything looks nice. Perhaps Douglas could update the help file in
> SASmixed, 
> where I copied the misleading code!

Umm, my version of that help file has

\examples{
options(
  contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
data(Weights)
fm1Weight <- lme( strength ~ Program * Time,
                  data = Weights, random = ~ 1 | Subj)
summary( fm1Weight )               # compare with output 3.1, p. 91
VarCorr( fm1Weight )
anova( fm1Weight )
fm2Weight <- update( fm1Weight, random = ~ Time | Subj )
anova( fm1Weight, fm2Weight )
summary( fm2Weight )
VarCorr( fm2Weight )
intervals( fm2Weight )
}

Notice the first line.
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