R’s formula interface is sweet but sometimes confusing. ANOVA is seldom sweet and almost always confusing. And random (a.k.a. mixed) versus fixed effects decisions seem to hurt peoples’ heads too. So, let’s dive into the intersection of these three.

I’m aware that there are lots of packages for running ANOVA models that make things nicer for particular fields. I’m just going to ignore them all here and focus on the builtin function **aov** and the standard mixed model package **lme4**. I’m not even going to talk about the analysis you might do with such models, still less delve into the horrors of Type 1/2/3 sums of squares. This is just the model specification part.

In the following, assume that Y is a dependent variable and A, B, C, etc. are predictors, all contained in data frame d.

## Formula Recap

If you use R then you probably already know this, but let’s recap anyway.

Start with an additive model of Y using the linear model function **lm**

lm(Y ~ A + B, data=d)

Interactions are expressed succinctly with the asterisk

lm(Y ~ A * B, data=d)

or equivalently but more explicitly by specifying component parts using the colon notation, like

lm(Y ~ A + B + A:B, data=d)

This is useful for more complex interaction structures, e.g.

lm(Y ~ A * B * C, data=d)

which contains all main effects, all two way interactions, and a three way interaction. On the other hand

lm(Y ~ A + B + C + A:B + A:C + B:C, data=d)

is the same except for having no three way interaction.

If you’re feeling fancy you can get the same effect as the model above by raising the variables to a power

lm(Y ~ (A + B + C)**2, data=d) lm(Y ~ (A + B + C)^2, data=d)

which, if you remember your algebra, amounts to the same thing. Frankly I find this a bit too clever, not least because

lm(Y ~ A + B + B**2, data=d)

does *not* specify a model with a linear effect of A and a quadratic effect of B as every beginning R user and everyone who takes the algebra analogy too seriously feels that it should.

Both of these specifications obey the *principle of marginality*, which requires, roughly, that all higher order interaction have their lower order siblings in the model unless you have a good reason. (Good reasons are shown below and tend to have to do with nesting). The asterisk and the power notation make sure your models obey this, whereas the colon invites you to forget something.

Squeezing the algebra analogy a bit further, another way to get the all two way interaction model is to make a three way model and then subtract the highest interaction term, like

lm(Y ~ A*B*C - A:B:C, data=d)

which is cute, but arguably not very useful.

Finally, remember that an intercept is almost always a good idea due to the principle of marginality, so R adds one by default and represents it with a 1. Consequently, these formulae specify the same model

lm(Y ~ A + B, data=d) lm(Y ~ 1 + A + B, data=d)

In the model matrix the intercept really is a column of ones, but R uses it rather more analogically as we will see when specifying mixed models.

In the unlikely event we want to remove the intercept, it can be replaced by a zero, or simply subtracted. Consequently these formulae specify the same, not very sensible, model:

lm(Y ~ 0 + A + B, data=d) lm(Y ~ A + B - 1, data=d) lm(Y ~ -1 + A + B, data=d)

OK, enough warm up. On to the ANOVAs

## Classical ANOVA

We start with simple additive fixed effects model using the built in function **aov**

aov(Y ~ A + B, data=d)

To cross these factors, or more generally to interact two variables we use either of

aov(Y ~ A * B, data=d) aov(Y ~ A + B + A:B, data=d)

So far so familiar. Now assume that B is nested within A

aov(Y ~ A/B, data=d) aov(Y ~ A + B %in% A, data=d) aov(Y ~ A + A:B, data=d)

so, nesting amounts to adding one main effect and one interaction.

### Random Effects in Classical ANOVA

**aov** can deal with random effects too, provided everything is nicely balanced. Assume A is a lone random effect, e.g. a subject indicator

aov(Y ~ Error(A), data=d)

Now assume A is random, but B is fixed and B is nested within A.

aov(Y ~ B + Error(A/B), data=d)

or maybe B and X are crossed (interacted) within levels of random A.

aov(Y ~ (B*X) + Error(A/(B*X)), data=d)

Or perhaps B and X within random A are categorized by (non-nested) G and H:

aov(Y ~ (B*X*G*H) + Error(A/(B*X)) + (G*H), data=d)

Yuck. This **Error** business can get confusing and the balance requirements tiresome, so for random effects models its usually easier to move to **lme4**.

## Mixed and Multilevel Models

Let’s start again with the lone random effects model

lmer(Y ~ 1 + (1 | A), data=d)

Random effects, like **(1 | A)**, are parenthetical terms containing a conditioning bar and wedged into the body of the formula. As the notation suggests, this is a conditional distribution of possible case level intercepts for each level or quantity of A. Still, the semantics should be familiar: **(B | A)** is equivalent to **(1 + B | A)** and the way to *not* automatically get an intercept added is to specify **(0 + B | A)** or perhaps more confusingly **(B – 1 | A)**.

Now assume that B is fixed but A is random

lmer(Y ~ B + (1 | A), data=d) lmer(Y ~ 1 + B + (1 | A), data=d)

Now let’s reconsider nested variables. If A is random, B is fixed, and B is nested within A then

lmer(Y ~ B + (1 | A:B), data=d)

Now the advantage of using **lmer **is that it is easy to state the relationship between two random effects. For example, if A and B are both random and *crossed* i.e. marginally independent, then

lmer(Y ~ 1 + (1 | A) + (1 | B), data=d)

Interestingly, if levels of (random) B are nested within levels of (random) A then the formula *looks* very much the same. However, this leads to an ambiguity.

Assume each level of A nests six levels of B, for example if we took six samples (B) from each of five subjects (A). If we label each subject’s samples 1, 2, 3, 4, 5, and 6 then although there are five subjects with B=1 these samples are completely unrelated. Unfortunately, this is just the way the data would look if A and B were actually crossed factors.

Bates suggests avoiding this design ambiguity by generating a new variable to represent the nesting. In general, this is just A:B, just as it was above.

lmer(Y ~ 1 + (1 | A) + (1 | A:B), data=d) lmer(Y ~ 1 + (1 | A/B), data=d)

This expresses the nesting and ensures that we don’t accidentally do a crossed factor analysis.

Moving further into multilevel regression territory, let’s assume that A is random and both the intercept and the slope of B depend on A. With no constraint on the slope-intercept relationship we have

lmer(Y ~ 1 + B + (1 + B | A), data=d)

but if we want to force B’s intercept and slope to be independent conditional on A then

lmer(Y ~ 1 + B + (1 | A) + (0 + B | A), data=d)

Here is a rare situation where it is sensible to remove an intercept, but only because the other random effect looks after it.

The second is a more parsimonious model but of course we’d want to check that the we weren’t missing anything important by making slope and intercept independent.

## Sources and Further Reading

Much of this information was gleaned from the personality-project‘s pages on doing ANOVA in R, from various Doug Bates course handouts, e.g. this one, and an R News article (pp.27-30), and from experimentation. A more ANOVA-focused piece is at statmethods.

You say:

“if levels of (random) B are nested within levels of (random) A then the formula is exactly the same. However, if both A and B are random and B is nested in A then the simple random intercept model is”

What is the difference between “levels of (random) B are nested within levels of (random) A” and “both A and B are random and B is nested in A”?

Thanks!

You’re right, that was rather vague. I’ve altered the text to try to make things clearer. Also I’ve added a link to some slides at the end that give a more thorough presentation, in case that’s useful.

Hi, thanks for your post which is very clear and relevant to my data.

A question, though, how would you code a two-way anova with interaction to which a random effect is applied? I.e. I got x1 and x2 explaining my y, and a z variable that have an influence on the whole experience.

`lmer(y ~ x1*x2 + (1|z), data = my.data)`

Would it be correct? If so, do you have recommendations regarding post-hoc tests that could apply to this formulation? I know the glht() function from the multcomp package can cope with

`lmer(y ~ x1 + x2 + (1|z), data = my.data)`

but as I tried to add the interaction, glht() won’t work anymore…Any help is welcome and thanks again for your post.

Best,

Philippe

I can’t really suggest anything about the multcomp package since I’ve never used it. My general feeling about multiple comparisons issues is summarised in Gelman et al. 2012, though I appreciate that may not be helpful to you.

Hello,

Thanks for your post. I’m fairly new to R and if you could help me out with a permutation of something that you’ve already posted, I’d appreciate it.

If I wanted: A to be fixed, B to be random and B nested within A, how would I do that using lmer?

Thanks so much!

I think this discussion might be relevant to your question, particularly the last answer.

Thank you for a clear explanation with code examples. Of all the lme4 tutorials I’ve seen, you break it down the best. Thanks for saving my sanity!

Hi, great post!

Is there a way to do all this on nonparametric data? (strongly heteroskedastic in my case)

I like your post, thank you.

Please advise me.

Consider fixed A and B, with A nested in B.

B has levels “red” and “green”.

I need separate p-values for the effect of A in red and the effect of A in green.