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

[Timothy MacKenzie]

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
>
> ///////////////////////////////////////////////////////////////////////////////////////////
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> ///////////////////////////////////////////////////////////////////////////////////////////
>
>
>
>
> 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")
>
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