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

[Phillip Alday]

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())
>
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