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 ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
