[R-sig-ME] lmer code for multiple random slopes

Ben Bolker bbo|ker @end|ng |rom gm@||@com
Tue Feb 16 16:04:28 CET 2021

   I second Phillip's point.  The example below works as expected (gets 
a singular fit, but there are 6 covariance parameters as expected). 
Based on what you've told us so far, the most plausible explanation is 
that one or both of your covariates (x and/or z) are factors 
(categorical) rather than numeric.

   Ben Bolker

dd <- data.frame(x=rnorm(500),z=rnorm(500),
form <- y ~ x + z + (x+z|g)
dd$y <- simulate(form[-2],

m1 <- lmer(form, data=dd)

On 2/16/21 8:18 AM, Phillip Alday wrote:
> 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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