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

[Ben Bolker]

  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]]
>>
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> 
> 
>