Message-ID: <1877a680-d190-7202-537d-90d51bc6574b@phillipalday.com>
Date: 2021-02-16T13:18:11Z
From: Phillip Alday
Subject: lmer code for multiple random slopes
In-Reply-To: <34JKCz24u8awawkhTLmboO_cBRWW-m0XAb0frTUb9YqspQe1VeBBWUvfqKd3-QIMLeKpiDUyzhN0X6aqRlEqwX1LmBfg3gn9EB2XCa5v62s=@protonmail.com>
I suspect we'll need to know a bit more about your data to answer this
question. Can you share it in any form (e.g. variables renamed and
levels of factors changed to something opaque) ?
Best,
Phillip
On 16/2/21 4:02 am, Peter R Law via R-sig-mixed-models wrote:
> I am trying to fit a model with two covariates, x and z say, for response y, with a random factor g and want each of x and y to have a random slope. I expected
>
> lmer(y ~ x + z + (x+z|g),...)
>
> to fit a model with 6 random variance components, the intercept, two slopes and three correlations. But I got an error message saying there were 74 random variance components and my data was insufficient to fit the model. Yet
>
> lmer(y ~ x + z + (x+z||g),...)
>
> returned what I expected, a model with the random intercept and two slopes but no correlations. How is lmer interpreting the first line of code above and how I would code for what I want. I have not been able to find any examples in the literature or online that help me but I may have easily missed something so if anyone knows of a useful link that'd be great. The only examples of multiple random slopes I've seen take the form
>
> lmer(y~x + z +(x|g) + (z|g),...)
>
> specifically excluding correlations between the random slopes and intercept of the two predictors. Even if the latter is a more sensible approach I'd like to understand the coding issue.
>
> Thanks.
>
> Peter
>
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