[R-sig-ME] Multi-level Rasch Model Per Douglas Bates' paper

[Ben Bolker]

    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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