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

[Ben Bolker]

   I'd have to look at this more carefully, but something like that.  
It's more robust and easier to understand if you use labels rather than 
numbers, e.g.

    coef(m1)$cond$item[["(Intercept)"]]

?  (if in doubt, use str(coef(m1)) to see how the pieces are arranged 
and labeled)

On 5/13/20 1:45 PM, Simon Harmel wrote:
> I meant *easiness *<- *coef(m1)[[1]][[1]][1]* *persons 
> <- coef(m1)[[1]][[2]][1]*
>
> On Wed, May 13, 2020 at 12:43 PM Simon Harmel <sim.harmel using gmail.com 
> <mailto:sim.harmel using gmail.com>> wrote:
>
>     Ben,
>
>     This is exactly what I'm trying to understand for my glmmTMB
>     models in my original post ('m1' & 'm2').
>
>     So to get the item easiness I think we need to use: *easiness *<-
>     *coef(f11)[[1]][[1]][1]*
>     *                              persons <- coef(f11)[[1]][[2]][1]*
>     *
>     *
>     Am I right Ben?
>
>     On Wed, May 13, 2020 at 12:32 PM Ben Bolker <bbolker using gmail.com
>     <mailto:bbolker using gmail.com>> wrote:
>
>             Apologies for not looking this over in great detail, but
>         not sure
>         why you're mixing lme4 and glmmTMB here??  Isn't item easiness
>         just
>         lme4::coef(fm2)$item ?
>
>         easiness <-
>            lme4::ranef(fm2)$item[[1]] +
>            glmmTMB::fixef(fm2)[imap$itype])
>
>         On 5/13/20 1:25 PM, Rasmus Liland wrote:
>         > On 2020-05-13 18:50 +0200, Rasmus Liland wrote:
>         >> Indeed it does work now!  Thanks!
>         > Right, so this code reproduces code until the
>         > easiness variable on page 15.  Perhaps this
>         > is useful?
>         >
>         > data("lq2002", package="multilevel")
>         > wrk <- lq2002
>         > # wrk[1:5,]
>         > for (i in 3:16) wrk[[i]] <- ordered(wrk[[i]])
>         > for (i in 17:21) wrk[[i]] <- ordered(5 - wrk[[i]])
>         > lql <- reshape(wrk,
>         >    varying = list(names(lq2002)[3:21]),
>         >    v.names = "fivelev",
>         >    idvar = "subj",
>         >    timevar = "item",
>         >    drop = names(lq2002)[c(2, 22:27)],
>         >    direction = "long")
>         > lql$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::glmer(
>         >    dichot ~ 0 + itype + (1 | subj) +
>         >      (1 | COMPID) +
>         >      (1 | item),
>         >    lql,
>         >    binomial))
>         > rr <- lme4::ranef(fm1, condVar = TRUE)
>         > str(rr$COMPID)
>         > head(rr$COMPID)
>         >
>         > qq <- lattice::qqmath(rr)
>         > print(qq$subj)
>         >
>         > ## 3.3 Allowing for interactions of company and item type
>         > (fm2 <- lme4::glmer(
>         >    dichot ~ 0 + itype + (1 | subj) +
>         >      (0 + itype | COMPID) +
>         >      (1 | item),
>         >    lql,
>         >    binomial))
>         >
>         > (fm3 <- lme4::glmer(
>         >    dichot ~ 0 + itype + (1 | subj) +
>         >      (1 | COMPID:itype) +
>         >      (1 | item),
>         >    lql,
>         >    binomial))
>         >
>         > (fm3a <- lme4::glmer(
>         >    dichot ~ 0 + itype + (1 | subj) +
>         >      (1 | COMPID:itype) +
>         >      (1 | COMPID) +
>         >      (1 | item),
>         >    lql,
>         >    binomial))
>         >
>         > anova(fm3, fm3a, fm2)
>         >
>         > str(imap <- unique(lql[, c("itype", "item")]))
>         >
>         > (easiness <-
>         >    lme4::ranef(fm2)$item[[1]] +
>         >    glmmTMB::fixef(fm2)[imap$itype])
>         >
>         > Best,
>         > Rasmus
>         >
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