[R-sig-ME] z-scores and glht

[Fox, John]

Hi Ben,

Specifying test="F" in car::linearHypothesis() should allow you to get a Wald F-test of a linear hypothesis (but without the Bonferroni correction, which I suppose could be done manually).

Best,
 John

--------------------------------------
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
Web: socialsciences.mcmaster.ca/jfox/




> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-
> project.org] On Behalf Of Ben Bolker
> Sent: Wednesday, April 25, 2018 2:35 PM
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] z-scores and glht
> 
> 
>   If someone wanted to work hard enough they could probably work out a
> Satterthwaite approximation for the degrees of freedom of these
> contrasts ... ?
> 
> 
> On 2018-04-25 02:25 PM, Dan Mirman 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
> >>
> >
> >
> >
> 
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