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())

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