[R-sig-ME] CLMM: Calculate ICC & Assessing Model Fit
Juho Kristian Ruohonen
juho@kr|@t|@n@ruohonen @end|ng |rom gm@||@com
Wed Aug 19 15:35:00 CEST 2020
Just voicing two thoughts as a non-statistician:
1. Why do you feel you need a mixed model in the first place? It looks to
me like every Individual is contributing the same number of responses (i.e.
4). Thus, the individual-specific effects can be assumed to cancel each
other out, and no random effect is needed.
2. Why complicate things with a non-canonical link function? It looks to me
like a standard ordinal logistic regression model, with fixed effects only,
would do the job for you. Guides and textbooks abound on how to assess the
fit of such models.
Best,
J
ke 19. elok. 2020 klo 14.44 Sidoti, Salvatore A. (
sidoti.23 using buckeyemail.osu.edu) kirjoitti:
> To begin with, I'm not a fan of cross-posting. However, I posted my
> question on Stack Exchange more than two weeks ago, but I have yet to
> receive a sufficient answer:
>
>
> https://stats.stackexchange.com/questions/479600/data-with-ordinal-responses-calculate-icc-assessing-model-fit
>
> Here's what I've learned since then (hopefully):
>
> 1) ICC of a CLMM:
> Computed like this:
> (variance of the random effect) / (variance of the random effect + 1) If
> this is correct, I would love to see a reference/citation for it.
>
> 2) 95% Confidence Interval for the ICC from a CLMM Model To my current
> understanding, a confidence interval for an ICC is only obtainable via
> simulation. I've conducted simulations with GLMM model objects ('lme4'
> package) and the bootMer() function. Unfortunately, bootMer() will not
> accept a CLMM model ('ordinal' package).
>
> 3) Model Fit of a CLMM
> Assuming that the model converges without incident, the model summary
> includes a condition number of the Hessian ('cond.H'). This value should be
> below 10^4 for a "good fit". This is straightforward enough. However, I am
> not as sure about the value for 'max.grad', which needs to be "well below
> 1". The question is, to what magnitude should max.grad < 1 for a decent
> model fit? My reference is linked below (Christensen, 2019), but it does
> not elaborate further on this point:
>
>
> https://documentcloud.adobe.com/link/track?uri=urn:aaid:scds:US:b6a61fe2-b851-49ce-b8b1-cd760d290636
>
> 3) Effect Size of a CLMM
> The random variable's effect is determined by a comparison between the
> full model to a model with only the fixed effects via the anova() function.
> I found this information on the 'rcompanion' package website:
>
> https://rcompanion.org/handbook/G_12.html
>
> The output of this particular anova() will include a value named
> 'LR.stat', the likelihood ratio statistic. The LR.stat is twice the
> difference of each log-likelihood (absolute value) of the respective
> models. Is LR.stat the mixed-model version of an "effect size"? If so, how
> does one determine if the effect is small, large, in-between, etc?
>
> Cheers,
> Sal
>
> Salvatore A. Sidoti
> PhD Candidate
> Behavioral Ecology
> The Ohio State University
>
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