[R-sig-ME] Fixed effects in nlmer

[marKo]

Hi Ben and group,

Is this a computational problem? How to solve it?
Do you have (on hand) some resources (links) to explain why? Used to see 
interaction used quite a bit in logistic models (not mixed models 
though, not recall any).

Thanks, and sorry for my ignorance.


Cheers,

Marko

On 17. 06. 2024. 15:32, Ben Bolker wrote:
>     Nice idea, but I don't see how that would work.  The terms in a 
> nonlinear mixed model aren't automatically expanded using linear model 
> formulas ...
> 
> On 2024-06-17 9:17 a.m., marKo via R-sig-mixed-models wrote:
>> Hi.
>>
>> How about adding it via an interaction effect?
>> Something like: y ~ SSlogis(x, Asym, Asym:lines_cathegorical, xmid, scal)
>> The rest should be more or less the same.
>>
>> Hope that it make sense.
>>
>>
>> Cheers,
>>
>> Marko
>>
>>
>>   On 17. 06. 2024. 15:05, Jarrod Hadfield wrote:
>>> Hi,
>>>
>>> I would like to use nlmer to fit logistic growth curves to data where 
>>> a number of replicate growth series are available for several clonal 
>>> lines. Fitting the random effect structure is straightforward:
>>>
>>> fm1 <- lme4:::nlmer(y ~ SSlogis(x, Asym, xmid, scal) ~ 
>>> (Asym+xmid+scal | line) + (Asym+xmid+scal | rep), ….)
>>>
>>> allowing all 3 growth parameters to vary across lines and across 
>>> replicates within lines.  However, I’ve had no success adding fixed 
>>> effects to the model formula, and the examples on the help page do 
>>> not have fixed effect predictors. For example, if lines could be 
>>> divided into two groups (A and B) how would I allow Asym to differ 
>>> between these two groups?
>>>
>>> Thanks for any help,
>>>
>>> Jarrod
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
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