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

[Ben Bolker]

  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