[R-sig-ME] Post hoc on glmer for specific hypotheses

[Timothy MacKenzie]

Thanks, Ben! Apparently, the 7th coefficient in the glmer() output
tests the first hypothesis that I'm looking for.

For the second hypothesis, I was able to use emmeans::emmeans() to get
what I want by running:

ems <- emmeans(m2, pairwise ~ time*item_type, type = "response",infer
= c(FALSE, TRUE), adjust = "tukey")[[2]]
vv <- pairs(ems, simple = "each", infer = c(FALSE, TRUE), type =
"response") # on log-odds scale

In this set up, vv[288, ] tests the second hypothesis. But on the Log
odds scale. Is there any way to convert it back to the response scale?

Thanks,
Tim M

On Wed, Mar 9, 2022 at 12:22 PM Ben Bolker <bbolker using gmail.com> wrote:
>
>   One way to do this would be via `multcomp::glht`, which allows you
> to specify any linear combination(s) of parameters to test.  You do
> have to figure out the association between the parameters you have and
> the contrasts you want. I wrote some (possibly scrutable) stuff about
> that problem here:
> http://bbolker.github.io/bbmisc/mgreen_contrasts.html
>
>   by hand:
>
>   assuming factor levels are {baseline, post} and {MC, prod}
>
> baseline_MC = intercept
> post_MC = intercept + time_post
> baseline_prod = intercept + gram_prod
> post_prod = intercept + time_post + gram_prod + interax
>
> so your first contrast would be {(intercept - (intercept + time_post))
> - (intercept + gram_prod - (intercept + time_post + gram_prod +
> interax))
>    = (-time_post -time_post - interax) = (-2*time_post - interax)
>
> so the contrast would be (0, -2, 0, -1) assuming that the parameters
> are ordered (intercept, time_post, gram_prod, interax)
>
>    (1) You should definitely check my algebra; (2) there may be a
> quicker (if less transparent) way to do this by setting up appropriate
> contrasts from the beginning; (3) I noticed that I left the "vocab"
> levels out of the analysis, but I don't think that changes anything
> important.
>
>
> On Wed, Mar 9, 2022 at 12:59 PM Timothy MacKenzie <fswfswt using gmail.com> wrote:
> >
> > Hello All,
> >
> > My glmer model below analyzes the performance of a single group of
> > subjects on a test at two time points. The test has 4 item types.
> >
> > Data and code are below.
> >
> > Is there a way to test only the following two hypotheses?
> >
> > 1- ((Baseline multiple-choice_grammar) - (Post-test
> > multiple-choice_grammar)) - (Baseline production_grammar - (Post-test
> > production_grammar))
> >
> > 2- ((Baseline multiple-choice_vocabulary) - (Post-test
> > multiple-choice_vocabulary)) - (Baseline production_vocabulary -
> > (Post-test production_vocabulary))
> >
> > dat <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/d.csv")
> >
> > form2 <- y ~ item_type*time + (1 | user_id)
> >
> > m2 <- glmer(form2, family = binomial, data = dat,
> >             control =
> >               glmerControl(optimizer = "bobyqa"))
> >
> > Sincerely,
> > Tim M
> >
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models