Global AI and Data Science

Hello, 
I have the following problem with my data.
I have two significant interaction terms in the model which accounts for the random effect of a group (small group interactions). However, R square, calculated as:
(random effects residuals - full model residuals)/ random effect residuals has horribly low values (.003 for the model with one interaction term, .01 for the model with two interaction terms). When I add these terms in a regular regression, both the total r-square and r-square changes are much higher. how can it be? 

I understand that regular regression does not separate random effects from fixed effects, however, the estimates are quite high.

Maybe my calculation of R square is not right? Any help? 

I've downloaded both the dataset and the results.
Cheers,
Karolina

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Karolina Ziembowicz
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#GlobalAIandDataScience
#GlobalDataScience

Just want to make sure I understand.  So you the random effects are not nearly as strong in terms of R-squared that you'd expect?  When you add random effects to a model with fixed effects, they do little to increase the R-Squared?

Shad...

------------------------------
Shad Griffin
------------------------------

Original Message:
Sent: Fri May 20, 2022 05:40 AM
From: Karolina Ziembowicz
Subject: linear mixed models - estimating R square

Hello, 
I have the following problem with my data.
I have two significant interaction terms in the model which accounts for the random effect of a group (small group interactions). However, R square, calculated as:
(random effects residuals - full model residuals)/ random effect residuals has horribly low values (.003 for the model with one interaction term, .01 for the model with two interaction terms). When I add these terms in a regular regression, both the total r-square and r-square changes are much higher. how can it be? 

I understand that regular regression does not separate random effects from fixed effects, however, the estimates are quite high.

Maybe my calculation of R square is not right? Any help? 

I've downloaded both the dataset and the results.
Cheers,
Karolina

------------------------------
Karolina Ziembowicz
------------------------------

#GlobalAIandDataScience
#GlobalDataScience

I looked through the output.  Everything looks right, granted I don't have much context.  Unfortunately, your r-squared is low and there isn't much difference between the groups in the data set.  Did you get a different result with a standard regression?  I didn't see output for that.

------------------------------
Shad Griffin
------------------------------

Original Message:
Sent: Fri May 20, 2022 05:40 AM
From: Karolina Ziembowicz
Subject: linear mixed models - estimating R square

Hello, 
I have the following problem with my data.
I have two significant interaction terms in the model which accounts for the random effect of a group (small group interactions). However, R square, calculated as:
(random effects residuals - full model residuals)/ random effect residuals has horribly low values (.003 for the model with one interaction term, .01 for the model with two interaction terms). When I add these terms in a regular regression, both the total r-square and r-square changes are much higher. how can it be? 

I understand that regular regression does not separate random effects from fixed effects, however, the estimates are quite high.

Maybe my calculation of R square is not right? Any help? 

I've downloaded both the dataset and the results.
Cheers,
Karolina

------------------------------
Karolina Ziembowicz
------------------------------

#GlobalAIandDataScience
#GlobalDataScience