[R-sig-ME] Reducing two mixed models into one

[João Veríssimo]

Hi Timothy,

- the rescaling of -1/RT was only to avoid very small estimates which 
can sometimes hurt convergence; -1000/RT is a bit more manageable and 
has an interpretation as rate in seconds.

- I used lmer() instead of blmer() only to get a hang on the basic 
setup. Indeed, if you're estimating random effects, setting a prior on 
the variance-covariace matrix might be helpful ... so you may be better 
off with blmer() (or with full bayesian sampling, as with brms/stan).

João

On 13/06/2023 18:38, Timothy MacKenzie wrote:
> Dear João,
>
> Thank you for your clarification. You made two changes in your code
> that I wanted to check with you.
>
> First, you rescaled the outcome from -1/RT to -1000/RT. I wonder in
> what way that improved the model?
>
> Second, instead of blmer(), you used lmer() but your lmer() random
> effects matrix is singular which makes me think if the correlation
> (~0.95) estimated by lmer() is as dependable/reliable?
>
> Thanks,
> Tim M
>
> On Tue, Jun 13, 2023 at 12:01 PM João Veríssimo<jl.verissimo using gmail.com>  wrote:
>> Hi Timothy,
>>
>> The formula I've suggested before is identical to the first parametrization in Thierry's answer, i.e., writing "item_num / Condition" is the same as writing "item_num + item_num:Condition".
>> That parametrization gives you estimates for the Condition slopes separately for each level of item_num. So the correlation between the corresponding by-subject random slopes is (I believe) what you want:
>>
>>> d <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/d3.csv")
>>> model_1 <- lmer(I(-1000/RT) ~ item_num / Condition + (item_num / Condition|Subject) + (item_num / Condition|Item), data = d)
>>> re.corrs <- attr(VarCorr(model_1)$Subject, "correlation")
>>> re.corrs["item_numEven:Conditionunrelated", "item_numOdd:Conditionunrelated"]
>> [1] 0.9499885
>>
>>
>> João
>>
>> On 13/06/2023 18:36, Timothy MacKenzie wrote:
>>
>> Dear Thierry,
>>
>> Thank you so much for your highly informative answer. If I may, I
>> wanted to ask a follow-up question.
>>
>> Previously, from the two separate models, I used to compute a
>> correlation (0.849635) between the random slopes of subjects in
>> 'Condition==unrelated' for Odd vs. Even items (shown below).
>>
>> **Question: Could we obtain the latent equivalent of the above
>> correlation (which may not be numerically the same as 0.849635) from
>> `attr(VarCorr(First_Parametrization_Model)$Subject, "correlation")`?
>>
>> Thank you so much again,
>> Tim M
>>
>> ranef_model_2_even = data.frame(ranef(model_2)$Subject)
>> ranef_model_2_even$Subject <- row.names(ranef_model_2_even)
>> ranef_model_3_odd = data.frame(ranef(model_3)$Subject)
>> ranef_model_3_odd$Subject <- row.names(ranef_model_3_odd)
>>
>> Subject = merge(ranef_model_2_even, ranef_model_3_odd, by = "Subject",
>> suffixes = c("_even", "_odd"))
>> cor(Subject$Conditionunrelated_even, Subject $Conditionunrelated_odd)
>> #  [1] 0.849635
>>
>>
>>
>>
>>
>> On Mon, Jun 12, 2023 at 1:36 AM Thierry Onkelinx
>> <thierry.onkelinx using inbo.be>  wrote:
>>
>> Dear Timothy,
>>
>> Add the interaction with item_num to every parameter and you should have the same parameterization for both models in a single model.
>>
>> # gives similar parameters as both models
>> I(-1/RT) ~ item_num + item_num:Condition + (item_num + item_num:Condition|Subject) + (item_num + item_num:Condition|Item)
>> # same model fit, different parametrization
>> I(-1/RT) ~ item_num*Condition + (item_num*Condition|Subject) + (item_num*Condition|Item)
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Statisticus / Statistician
>>
>> Vlaamse Overheid / Government of Flanders
>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST
>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>> thierry.onkelinx using inbo.be
>> Havenlaan 88 bus 73, 1000 Brussel
>> www.inbo.be
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>> To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>>
>>
>>
>> Op za 10 jun 2023 om 19:06 schreef Timothy MacKenzie<fswfswt using gmail.com>:
>>
>> Hello All,
>>
>> I'm hoping to clarify my prior post to elicit an informative response
>> from the experts on the list.
>>
>> Currently, I'm running two models each using a subset of my data (below).
>>
>> <Question>: Instead of running two separate models, is it possible to
>> create one model that captures both these separate models?
>>
>> Thank you,
>> Tim M
>> ################
>> d = read.csv("https://raw.githubusercontent.com/fpqq/w/main/d3.csv")
>> library(optimx)
>> library(blme)
>>
>> # Subset 1:
>> model_2 = blmer(I(-1/RT) ~ Condition + (Condition|Subject) + (Condition|Item),
>> data = d, control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
>>             subset = item_num == "Even")
>>
>> # Subset 2:
>> model_3 = blmer(I(-1/RT) ~ Condition + (Condition|Subject) + (Condition|Item),
>> data = d, control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
>>               subset = item_num == "Odd")
>>
>> _______________________________________________
>> R-sig-mixed-models using r-project.org  mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>> _______________________________________________
>> R-sig-mixed-models using r-project.org  mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>

-- 
*João Veríssimo*
Assistant Professor | /Professor Auxiliar/
School of Arts and Humanities | /Faculdade de Letras/
University of Lisbon | /Universidade de Lisboa/
	[[alternative HTML version deleted]]