[R-sig-ME] mixed-effects model with partially nested fixed effects

[Jake Westfall]

Hi Yuqi,

Because each subject has only a single row, you don't need random effects
for subjects. You do have observations nested in experiments, so you
probably want some sort of experiment effects. Right now you have fixed
experiment effects, but random effects probably make more sense.

The maximal model
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.703.657&rep=rep1&type=pdf>
w/
random experiment effects would be like:
lmer(DV ~ movement_type*motor_effort + (movement_type*motor_effort|
experiment))

In case of convergence problems, you can probably drop the random
movement_type*motor_effort
interaction, but usually it's best to decide what to drop by eyeballing the
nonconverged results to see which random effects seem extraneous (due to
either 0 estimated variance or perfect estimated correlations with other
random effects).

As for how to center, it depends on what you're interested in. A good
reference here is Enders & Tofighi
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.928.9848&rep=rep1&type=pdf>
.

Jake


On Mon, Oct 9, 2017 at 9:20 AM, Yuqi Liu <yliu at psych.udel.edu> wrote:

> Hi R-experts,
>
>   I am analyzing data from 3 experiments using linear mixed model. Each
> experiment had 2 fixed effects, movement type and motor effort. Movement
> type is shared by all experiments, motor effort varied but partially shared
> across experiments. The data looks like this:
>
>
>   Subject              experiment        movement_type
> motor_effort     DV
>
>   1                           1                              synchronous
>                     normal
>   2                           1                              asynchronous
>                   normal
>    .
>    .
>    .
>
>   101                       2                              synchronous
>                   normal
>   102                       2                              asynchronous
>                 normal
>   103                       2                              synchronous
>                   medium
>   104                       2                              asynchronous
>                 medium
>    .
>    .
>    .
>   201                       3                              synchronous
>                   normal
>   202                       3                              asynchronous
>                 normal
>   203                       3                              synchronous
>                   hard
>   204                       3                              asynchronous
>                 hard
>    .
>    .
>    .
>   I want to test if the effect of movement type differed across
> experiments and motor effort, but I am a little confused of how the model
> should be constructed in this case.
>
>   My current model is :   model <- lmer(DV ~ movement_type *
> experiment/motor_effort + (1|subject), data). Is that correct in terms of
> my purpose?
>
>   I am also wondering how to center independent variables in this case.
> For movement_type, I could code "synchronous" as "0.5" and "asynchronous"
> as "-0.5". For motor_effort, should I center them for each experiment
> separately, or across all experiment?
>
> Thank you for your help!
> Yuqi
>
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]