[R-sig-ME] z-scores and glht
Cristiano Alessandro
cri.alessandro at gmail.com
Wed Apr 25 20:49:39 CEST 2018
Hi Dan,
thanks for your answer. Sorry about my naive question, from a
non-statistician. I still have trouble understanding; you say that z-scores
are the estimates divided by the SE. Isn't this the definition of a
t-statistic under the null hypothesis that the mean is equal to zero?
Also, when you say that glht() is side-stepping all of that and just using
a normal approximation. What does it mean/imply exactly, as far as
computing the z-scores (the ones I see in the output of the summary) goes?
Best
Cristiano
On Wed, Apr 25, 2018 at 1:25 PM, Dan Mirman <dan at danmirman.org> wrote:
> The z-scores are computed by dividing the Estimate by the SE. As for why
> these are not t-statistics, the short answer is that the degrees of freedom
> are not trivial to compute. I believe Doug Bates' response is often cited
> by way of explanation:
> http://stat.ethz.ch/pipermail/r-help/2006-May/094765.html and it is
> covered
> in the FAQ:
> http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#
> why-doesnt-lme4-display-denominator-degrees-of-freedomp-values-what-other-
> options-do-i-have
> (for more discussion of alternatives see Luke, 2017,
> http://link.springer.com/article/10.3758%2Fs13428-016-0809-y).
>
> glht() is side-stepping all of that and just using a normal approximation.
> For what it's worth, my own experience is that this approximation is only
> slightly anti-conservative, so I usually feel comfortable using it.
>
> Hope that helps,
> Dan
>
> On Wed, Apr 25, 2018 at 12:26 PM, Cristiano Alessandro <
> cri.alessandro at gmail.com> wrote:
>
> > Hi all,
> >
> > something is wrong with my email, so I am sorry for possible multiple
> > postings.
> >
> > After fitting a model with lme, I run post-hoc tests with glht. The
> results
> > are repored in the following:
> >
> > > lev.ph <- glht(lev.lm, linfct = ph_conditional);
> > > summary(lev.ph, test=adjusted("bonferroni"))
> >
> > 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 3232.2 443.2 7.294 9.05e-13 ***
> > des_days14 == 0 3356.1 912.2 3.679 0.000702 ***
> > des_days48 == 0 2688.4 1078.5 2.493 0.038025 *
> >
> > I am trying to understand the output values. How are the z-scores
> computed?
> > If the function uses standard errors, should these be t-statistics (and
> not
> > z-scores)?
> >
> > Thanks for your help, and sorry for the naive question.
> >
> > Best
> > Cristiano
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
>
>
> --
> -----------------------------------------------------
> Dan Mirman
> Associate Professor
> Department of Psychology
> University of Alabama at Birmingham
> http://www.danmirman.org
> -----------------------------------------------------
>
> [[alternative HTML version deleted]]
>
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