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.

Thankfully a paper in Methods in Ecology and Evolution by Nakagawa & Schielzeth helped to get close to solving this problem.

The only problem was that the code was a little difficult to implement.

Now Kamil Barton has written a function for this calculation in my current favourite R package – MuMIn. (Edit – as Erika Berenguer pointed out on twitter this doesn’t work for all versions of MuMin only for versions later than 1.19. So if you’ve got an older version use the update.packages() function.)

Basically the function works like this:

- You give it your mixed model
- It spits out marginal and conditional R squared values

The marginal R squared values are those associated with your fixed effects, the conditional ones are those of your fixed effects plus the random effects. Usually we will be interested in the marginal effects.

To show how this works we’ll use the example given in the paper using beetle body length.

First get the dataset we will use from here.

#first load in the packages you want
require(lme4)
require(MuMIn)
#Next the data
BeetleBody<-read.csv("Wherever_you_put_the_data")
# Fit a model including fixed and all random effects
mF <- lmer(BodyL ~ Sex + Treatment + (1 | Population) + (1 | Container), data = BeetleBody)
r.squaredGLMM(mF)

This should spit out the following:

R2m R2c

0.3913021 0.7406447

Showing that your marginal R squared is 0.39 and your conditional R squared is 0.74 – not bad.

Of course you should do model simplification or model averaging in an attempt to get a parsimonious model before you do any of this, but I just wanted to flag this up.

Apparently this function is still in it’s ‘experimental stages’ so if you manage to break it let the people who put MuMIn together know.

If you have any comments, as ever put them below.

I’ll get back to writing a non-stats blog post soon.

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