A short primer on fixed and random effects estimators in R with four examples from Wooldridge (2013).

## Tags » Random Effects

#### Laplace approximation in Python: another cool trick with PyMC3

I admit that I’ve been skeptical of the complete rewrite of PyMC that underlies version 3. It seemed to me motivated by an interest in using unproven new step methods that require knowing the derivative of the posterior distribution. 66 more words

#### Cloud Computing for Christmas

My second book – R for Cloud Computing : An Approach for Data Scientists is now ready for sale ( ebook). Softcover should be available within a month. 79 more words

#### Intraclass Correlation (ICC): Which formula?

The intraclass correlation coefficient (ICC) is a useful descriptive statistic for measuring the strength of the relationship between the observations within a class or group. It is often used, for example, to assess reliability in interrater reliability studies. 719 more words

#### The new Mix: Mixed Effects Models

So what are mixed effects models and how do they relate to GEE models? Well, they are called mixed effects models because they contain both fixed effects and random effects. 435 more words

#### R squared for mixed models - the easy way

Earlier this year I wrote a post on calculating R squared values for mixed models.

It turned out a lot of people had been having the same problem that I had been having – basically we didn’t know how well our mixed models fit our data. 321 more words

#### Mixed Models in R: lme4, nlme, or both?

The topic of Mixed Models is an old-friend of this blog, but I want to focus today on the R code for these models.

Amongst all the packages that deal with linear mixed models in R (see… 989 more words