[R-sig-ME] adjusted values

[Cristiano Alessandro]

Thanks Russ,

point (1) was clear to me; point (2) a bit less, so thanks for the hint. I
do only care about comparing with the -1 level. The question is whether I
should do multiple comparison corrections with glht (as I did so far), or
take the p-values of the estimated coefficients. Do you have suggestions
here?

Best
Cristiano

On Thu, Mar 22, 2018 at 4:05 PM, Lenth, Russell V <russell-lenth at uiowa.edu>
wrote:

> Cristiano,
>
> First, I want to make clear that you understand that (1) the intercept is
> really the estimate of the mean when des_days = -1, and that (2) the
> multiplicity correction used in the test is based on the multivariate t
> distribution (not Bonferroni). As such, it is less conservative than
> Bonferroni, and is "exact" in that if the underlying assumptions are
> exactly true, the probability of at least one type-I error is controlled at
> the desired level. (That said, the P values are actually computed using a
> simulation method, so they will vary a bit if you call glht again).
>
> If you really only care about comparisons with the -1 level, I think what
> you have is a good solution. Some people want to use weaker control of the
> error rate. In that case, you can use
>
>    summary(glht(...), test = adjusted("desired choice"))
>
> (see the help file for summary.glht), which gives you other choices
> besides the single-step method that it defaults to. You could in fact
> specify adjusted("none") to get no adjustments, or adjusted("bonferroni"),
> adjusted("fdr"), etc. if you want to use one of the standard methods in
> stats::p.adjust.methods.
>
> Often, people want to compare *all* pairs of treatments; and if that's the
> case, you can specify that using a call to mcp() in the linfct argument of
> glht.
>
> Russ
>
> Russell V. Lenth  -  Professor Emeritus
> Department of Statistics and Actuarial Science
> The University of Iowa  -  Iowa City, IA 52242  USA
> Voice (319)335-0712 (Dept. office)  -  FAX (319)335-3017
>
>
>
> On 18-03-22 01:28 PM, Cristiano Alessandro wrote:
> > Hi all,
> >
> > I am fitting a linear mixed model with lme4 in R. The model has a
> > single factor (des_days) with 4 levels (-1,1,14,48), and I am using
> > random intercept and slopes.
> >
> > Fixed effects: data ~ des_days
> >                  Value   Std.Error  DF   t-value p-value
> > (Intercept)  0.8274313 0.007937938 962 104.23757  0.0000
> > des_days1   -0.0026322 0.007443294 962  -0.35363  0.7237
> > des_days14  -0.0011319 0.006635512 962  -0.17058  0.8646
> > des_days48   0.0112579 0.005452614 962   2.06469  0.0392
> >
> > I can clearly use the previous results to compare the estimations of
> > each "des_day" to the intercept, using the provided t-statistics.
> > Alternatively, I could use post-hoc tests (z-statistics):
> >
> >> ph_conditional <- c("des_days1  = 0",
> >                       "des_days14  = 0",
> >                       "des_days48 = 0");
> >> lev.ph <- glht(lev.lm, linfct = ph_conditional);
> >> summary(lev.ph)
> >
> > Simultaneous Tests for General Linear Hypotheses
> >
> > Fit: lme.formula(fixed = data ~ des_days, data = data_red_trf, random
> > = ~des_days |
> >     ratID, method = "ML", na.action = na.omit, control = lCtr)
> >
> > Linear Hypotheses:
> >                  Estimate Std. Error z value Pr(>|z|)
> > des_days1 == 0  -0.002632   0.007428  -0.354    0.971
> > des_days14 == 0 -0.001132   0.006622  -0.171    0.996
> > des_days48 == 0  0.011258   0.005441   2.069    0.101
> > (Adjusted p values reported -- single-step method)
> >
> >
> > The p-values of the coefficient estimates and those of the post-hoc
> > tests differ because the latter are adjusted with Bonferroni
> > correction. I wonder whether there is any form of correction in the
> > coefficient estimated of the LMM, and which p-values are more
> appropriate to use.
> >
> > Thanks
> > Cristiano
>
>

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