Multi-level Rasch Model Per Douglas Bates' paper

On 13/5/20 5:07 pm, Simon Harmel wrote:

Hi All!
I'm following this paper 
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."*

Dear Simon,

I was not even able to get past subsection 3.2 ...
how reproducible even are the examples in 
this text?

Best,
Rasmus

data("lq2002", package="multilevel")
wrk <- lq2002
# wrk[1:5,]
for (i in 3:16) wrk[[i]] <- ordered(wrk[[i]])
for (i in 17:21) wrk[[i]] <- ordered(5 - wrk[[i]])
lql <- reshape(wrk,
  varying = list(names(lq2002)[3:21]),
  v.names = "fivelev",
  idvar = "subj",
  timevar = "item",
  drop = names(lq2002)[c(2, 22:27)],
  direction = "long")
lql$itype <-
  with(lql, factor(ifelse(item < 12, "Leadership",
    ifelse(item < 15, "Task Sig.", "Hostility")
  )))
for (i in c(1, 2, 4, 5)) lql[[i]] <- factor(lql[[i]])
lql$dichot <- factor(ifelse(lql$fivelev < 4, 0, 1))
# str(lql)
# summary(lql)

## 3.2 Fitting an initial multilevel Rasch model
(fm1 <- lme4::lmer(
  dichot ~ 0 + itype + (1 | subj) + (1 | COMPID) + (1 | item),
  lql,
  binomial))
Error in mkRespMod(fr, REML = REMLpass) : response must be numeric
Calls: <Anonymous> -> do.call -> <Anonymous> -> mkRespMod
Execution halted