[R-sig-ME] Different results for between/within groups and within group regression analyses

[Luca Danieli]

Hi Phillip,

sorry if I ask you a question.
In this moment I have a 3x4x8 matrix, where '3' is the number of groups, '8' the number of tests, and '4' the levels of the potential main effect.

Following your explanation, I was before thinking that leaving interactions out of the models would give you a better approximation of the main effect. But now that I read it again, I am unsure about it.

In my case, the '4' levels are nested in each set.
If I write lmer(Score ~ pot_ME + random effects) I have no statistical significance.
If I write lmer(Score ~ pot_ME*groups + random effects) I have statistical significance for the main effect (p<0.05) and a strong interaction (p<.001).
If I write lmer(Score ~ pot_ME*groups*tests + random effects) I have no statistical significance nor interactions.

What approach is the more correct to get information about the presence of a main effect?

(My parameters are not-continuous)

Best
Luca
________________________________
From: Alday, Phillip <Phillip.Alday at mpi.nl>
Sent: 11 January 2018 15:38
To: Luca Danieli; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Different results for between/within groups and within group regression analyses

I'll do it myself when I get the chance in the next day or so.  :-)

Phillip

________________________________
From: Luca Danieli <mr.lucedan at hotmail.it>
Sent: Thursday, January 11, 2018 10:26 AM
To: Alday, Phillip; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Different results for between/within groups and within group regression analyses

Thank you Phillip!

Can I add your answer to CrossValidated for future concerns by other users?

Best
Luca
________________________________
From: Alday, Phillip <Phillip.Alday at mpi.nl>
Sent: 11 January 2018 15:18
To: Luca Danieli; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Different results for between/within groups and within group regression analyses

By only using one group, you're changing the amount of pooling going on, which affects shrinkage and the bias-variance / over- vs. underfitting tradeoff. When you fit a model to a subset, it will generally be better at describing that subset but often worse at describing the full set / other sets. In other words, your subset model better describes the subset because it doesn't have to spend "resources" describing the other data, but of course this also means that it will tend to not describe the other data as well - it's better at the small details but worse at the big picture.

Best,
Phillip

Sent from my mobile, please excuse my brevity.

________________________________
From: Luca Danieli <mr.lucedan at hotmail.it>
Sent: Thursday, January 11, 2018 10:10 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Different results for between/within groups and within group regression analyses


Dear all,

from CrossValidates I was suggested to repost my question to you, as it is a technical question about R and mixed models.
Particularly, as I have a thesis to hand in in a few weeks, I hope you are able to help me understanding some problems that I cannot figure out by myself.

In this case, I have used the function lmer() to look for an interaction between groups and then used the function predict() to plot the fits for each group on a graphic.
Then I applied the lmer() to just one of those groups (same formula, technically) and used the predict() function to plot the fits for that specific group. I was thinking to obtain the same graphic for that group type and instead I obtained two different results.

I explained the process, models and presented the plots in this post:
https://stats.stackexchange.com/questions/322608/different-results-for-between-within-groups-and-within-group-regression-analyses

Can somebody help me understand this?

Best
Luca



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