by n_ro
Last Updated June 12, 2019 08:19 AM

I have a repeated-measures design where participants were measured 4 times each on 2 consecutive days. There were 2 conditions, randomly attributed to day 1 and day 2 for each participant.

So, this is how the head of my data 'dat' look:

'subject' is just the IDs, 'DV' is my outcome/dependent variable.

I want to know how 'day', condition ('cond'), and 'measurement' influence 'DV' using R's lme package. Even after a rather excessive search, I'm still not sure how to set the random effects...

So far, I've done something like:

```
m_base <- lme(DV ~ 1, random = ~1|subject/cond/measurement, data = dat, method = "ML")
```

or

```
m_base <- lme(DV ~ 1, random = ~1|subject/day/measurement, data = dat, method = "ML")
```

and then stepwise added my variables as fixed effects using `update()`

and compared the models using `anova()`

.

Still, I'm **not sure about how to set the random effects**, since 'measurement' is somehow nested within both 'day' and 'cond'. Does anyone know a correct way of implementing my question?

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