[R-sig-ME] Specifying models nested crossed random effects

Sat Apr 8 21:28:07 CEST 2017

Josh,
Thanks for the questions.
Can you provide a little bit more description about the variables?
Does "site" = "program"?
Are participants queried at multiple timepoints? If pre- and post-program,
could this be included as a factor with levels "before" and "after"?
Do you have any particular hypotheses or questions you want to answer with
your model?
Best wishes, Evan

On Sat, Apr 8, 2017 at 11:27 AM, Joshua Rosenberg <
jmichaelrosenberg at gmail.com> wrote:

> Hi r-sig-mixed-models,
>
> I am new to the group and have a question about crossed random effects for
> student, sample, and program sources of variation.
>
> In my study of out-of-school programs, our outcomes are continuous measures
> of participant's interest and engagement (we envision that different
> outcomes will be analyzed as part of separate models).
>
> In specific, our data consists of:
>
>    - About 20 individuals per program
>    - About 10 programs
>    - Within each site, about 20 samples (the samples were at the same time
>    for all of the individuals within the program, but at different times at
>    different programs)
>
> Because there are dependencies by both participant, sample, and program, we
> think there are two crossed random effects, one for observations associated
> with each individual, and one for observations associated with each sample.
> Both of these random effects are nested in one of the 10 programs.
>
> The data look like the following:
>
> # A tibble: 2,970 × 4
>    overall_engagement participant_ID program_ID      sample_ID
>                 <dbl>         <fctr>     <fctr>         <fctr>
> 1            2.833333           1001          1 1-2015-07-14-1
> 2            2.833333           1001          1 1-2015-07-14-2
> 3            2.500000           1001          1 1-2015-07-15-1
> 4            2.333333           1001          1 1-2015-07-15-2
> 5            3.000000           1001          1 1-2015-07-21-1
> 6            2.666667           1001          1 1-2015-07-21-2
> 7            3.000000           1001          1 1-2015-07-21-4
> 8            3.166667           1001          1 1-2015-07-22-1
> 9            3.833333           1001          1 1-2015-07-22-4
> 10           3.000000           1001          1 1-2015-07-28-1
> # ... with 2,960 more rows
>
>
> Our understanding from the nested or crossed section of the FAQ and the
> answer to this question is that because we have unique variables do have
> unique values of the sample, there seem to be two options for how we can
> specify the model using the lme4 package in R:
>
> 1. Not nesting the crossed random effects within the site because the
> sample variable includes a site identifier:
>
> lmer(interest ~ 1 + (1|participant_ID) + (1|sample_ID), data = df)
>
>
> 2. Creating the sample variable without a site identifier but in a way so
> that samples within each site were still identified uniquely and nesting
> the crossed random effects within the site:
>
> lmer(interest ~ 1 + (1|site/participant_ID) + (1|site/sample), data = df)
>
>
> Based on this question
> <http://stats.stackexchange.com/questions/96600/
> interactions-between-random-effects>
> (and
> some example models where this seemed to work), we were also curious about
> adding an interaction (to option 1 or option 2) between participant_ID
> and sample_ID by adding a random effect via (1|participant_ID:sample_ID).
>
> Does either of these seem like they would help to account for
> dependencies by participant, sample, and program?
>
> Please let me know if more information (or less!) would be helpful. Thank
> you for considering this.
>
> Josh
>
> --
> Joshua Rosenberg, Ph.D. Candidate
> Educational Psychology and Educational Technology
> Michigan State University
> http://jmichaelrosenberg.com
>
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>
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-- 
Evan Palmer-Young
PhD candidate
Department of Biology
221 Morrill Science Center
611 North Pleasant St
Amherst MA 01003
https://sites.google.com/a/cornell.edu/evan-palmer-young/
epalmery at cns.umass.edu
ecp52 at cornell.edu

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