Multi-level Rasch Model Per Douglas Bates' paper
?? Yes, this is cross-posted, and I was planning on getting around to showing how to do it in the Cross-Validated post. glmmTMB doesn't have a built-in dotplot method, but you can cheat pretty easily because the individual components of a ranef() extracted from a glmmTMB fit ($cond for conditional model, $zi for zero-inflation model if any) have the same structure as ranef() from lme4, so you can steal the plotting method: library(glmmTMB) example(glmmTMB) library(lme4) r <- ranef(m1) library(lattice) lme4:::dotplot.ranef.mer(r$cond) ? Note that it's not as easy to get dotplots with whiskers from coef() because of some long-standing (and deep) issues with computing standard deviations for the sum of a fixed and a random effect ...
On 5/13/20 11:28 AM, Phillip Alday wrote:
I think I saw this go past on CrossValidated -- you should mention any crossposting. :) In general, it would be nice to know what the structure of your data are. Is "gender" a property of your participants, items, or something else? What about item_type? In lme4, you can extract the item-level predictions with coef(m) (which is the same as ranef(m) + fixef(m)).? You can even get a plot of these with: library(lattice) dotplot(ranef(m, condVar=TRUE)) The zero-point is the grand mean (i.e. the corresponding fixed effect). I don't know if this is the same as in glmmTMB. Best, Phillip On 13/5/20 5:07 pm, Simon Harmel wrote:
Hi All! I'm following this paper <https://www.jstatsoft.org/article/view/v020i02> ( https://www.jstatsoft.org/article/view/v020i02) by Prof. Bates where after fitting the model (*pp. 14-15*), they obtain what they call *item easiness* *"from the estimates of the fixed effects and the conditional modes of the random effects."* In short, I wonder how to obtain item easiness estimates for each of my models (m1 & m2) below? *Thank you, Simon* library(glmmTMB) dat <- read.csv('https://raw.githubusercontent.com/ilzl/i/master/d.csv') form11 <- y ~ item_type + (1 | item_id) + (1 | person_id) form22 <- y ~ item_type + gender + (1 | item_id) + (1 | person_id) m1 <- glmmTMB(form11, data = subset(dat, person_id <= 40), family = beta_family()) m2 <- glmmTMB(form22, data = subset(dat, person_id <= 40), family = beta_family()) [[alternative HTML version deleted]]
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