[R-sig-ME] p-values from model fitting or glht?

[Cristiano Alessandro]

Hi all,

I am running a mixed model to compare two groups. This is repeated measure
design. The model is very simple:

linM11 <- lme(values ~ grf, random = ~1|id, data=dat_trf,
na.action=na.omit, method = "ML", control=lCtr )

where grf is a factor with two levels. I am asking if the two levels are
significantly different. When I call summary() I obtain (among the other
things):

> summary(linM11)

Fixed effects: values ~ grf
                 Value         Std.Error    DF  t-value      p-value
(Intercept) -8.513064 0.9908567 16   -8.59162   0.0000
grf1            3.027705 1.4158346 15     2.13846   0.0493

which makes me think that the two groups are barely significantly
different. But If I run this post-hoc test I get this other (quite
different) result:

> ph_conditional <- c("grf1 = 0");
> linM.ph <- glht(linM, linfct = ph_conditional);
> summary(linM.ph)

Linear Hypotheses:
                  Estimate   Std. Error   z value    Pr(>|z|)
grf1 == 0    3.028        1.372         2.206      0.0274 *

Which one should I trust? I am always confused on whether I should use the
p-values of the model fit or those of the post-hoc tests. If I had multiple
tests, I would certainly run the post-hoc and adjust for multiple
comparisons (with glht). But here there is only one test, and I am not sure
why I get so different results, and which one I should trust.

Thanks
Cristiano

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