Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- R-Square for Individual Fixed Effect Estimates in ...

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

07-10-2017 03:47 PM

Hello,

I am trying to get % variance explained for the fixed effects from my proc mixed code for a mixed linear regression. Unfortunately, I do not know how to request this in my output. Here is an example of my current code:

Proc Mixed Data=Set covtest;

Class ClassVar;

Model Outcome = Predictor1 Predictor2 Predictor3/solution ddfm=kr;

Random intercept/subject=pedid;

Run;

If there is anyway to get this information in my output that would be great.

Thanks,

Jer

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

07-10-2017 03:52 PM

There is no such thing as an R-squared value for fixed effects. You get an R-squared value for an entire model being fit (and as far as I remember, you can't get this from PROC MIXED).

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

07-11-2017 10:51 AM

Paige is correct. There have been pseudo r-squares proposed for mixed models, but none have really been accepted in the statistical literature. If someone has a reference for a good one, post it here.

You can get r-squares for effects if you use type 1 statistics in a GLM, but again these r-squares are not available in a mixed model.

Random effects are a different story. There have been ways proposed to evaluate the relative variation explained by each random effect in the overall error variance, if the covariance structures involved are simple.