[R-sig-ME] lme approximation method for dfs
M@@rten@Jung @end|ng |rom m@||box@tu-dre@den@de
Sun May 24 00:01:07 CEST 2020
As Phillip Alday explained, there is no implementation of the Satterthwaite
approximation in the nlme package. If you want to stick with this package,
the only way I know to get something similar (for lme objects) is to use
functions of the emmeans package with the argument "mode" set to
"appx-satterthwaite" (see ).
On Sat, 23 May 2020, 17:07 Phillip Alday <phillip.alday using mpi.nl> wrote:
> On 23/5/20 9:11 am, Salahadin Lotfi wrote:
> > Dear all,
> > I have a very simple question but, have been having a hard time to figure
> > it out.
> > I am using a mixed model with random intercept and slope using lme
> > with an unstructured covariance matrix. I know lmer uses Satterthwaite's
> > approximation method to approximate dfs of fixed effects,
> This is not accurate. lme4 by default doesn't even try to figure out the
> df and doesn't report p-values. The lmerTest package adds in options to
> use Satterthwaite or Kenward-Roger approximations for p-values, but
> depending on who you ask around here, the sentiment for those
> approximations ranges from "of course" to "hmrpf, why would you bother?"
> to "the heretics must be purged!".
> The GLMM FAQ (https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html)
> has some info on each of these, but I'll copy and paste something
> relevant that I wrote on a different mailing list:
> Treating the t-values as z-values is as reasonable as using the
> t-distribution with some estimated degrees of freedom for studies with
> 20-30 subjects and 10s of observations per condition per subject for two
> reasons. One is that a t-distribution with dozens of degrees of freedom
> is essentially a normal distribution, and so even if you could figure
> out what the "right" number of degrees of freedom were, it wouldn't be
> far off from the number you get from the normal distribution. The other
> reason is that none of these asymptotic results are guaranteed to be
> particularly great for anything other than very well behaved linear
> mixed models, which is why things like parametric bootstrap are the gold
> standard for figuring out coverage intervals. And for large models,
> bootstrapping is about as fast as KR (because KR as implemented in
> pbkrmodcomp, which lmerTest depends on, computes the inverse of a large
> n x matrix).
> > but I am not sure
> > what is the preferred method that lme uses. Is it Wald or Likelihood
> Wald and likelihood ratio are not degrees of freedom estimates. The
> likelihood-ratio tests do have a df, which corresponds to the difference
> in the number of free parameters between the models, but this not the
> relevant df. (It's numerator degrees of freedom in the ANOVA framework,
> while what you need are the denominator degrees of freedom.) The Wald
> tests are just the things you see in the table of the fixed effects,
> i.e. the tests corresponding to the t- or z-values (or more generally
> the ANOVA-style tests / tests of linear hypotheses you then construct
> from the fixed effects).
> > I don't think lme offers such an option to specify an approximation
> > for dfs of fixed effects. Does it?
> The dfs in nlme are computed using the "inner-outer" rule which doesn't
> work well for many types of designs common in cognitive neuroscience.
> More information on this is in the GLMM FAQ, search for "Df
> alternatives" on that page.
> Hope that helps!
> > I appreciate any response in advance.
> > Sala
> > *************
> > Salahadin (Sala) Lotfi
> > PhD Candidate of Cognitive Neuroscience
> > University of Wisconsin-Milwaukee
> > Anxiety Disorders Laboratory
> > President, Association of Clinical and Cognitive Neuroscience, UWM
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