How obtain the estimated scale parameter as in older lmer function version

Hello,

I read the article based on lme4

Doran H, Bates D, Bliese P, Dowling M (2007). ?Estimating the Multilevel Rasch Model: With
the lme4 Package.? Journal of Statistical Software, 20(2). URL http://www.jstatsoft.
org/v20/i02/.

and I saw that the print of 'mer' class showed a estimated scale parameter. Nowadays, the print doesn't show this. I tried calculate by myself employing Pearson residuals and deviance() function but this didn't match the same result. In the article there was this output:

R> data("lq2002", package = "multilevel")
R> wrk <- lq2002
R> for (i in 3:16) wrk[[i]] <- ordered(wrk[[i]])
R> for (i in 17:21) wrk[[i]] <- ordered(5 - wrk[[i]])
R> 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")
R> lql$itype <- with(lql, factor(ifelse(item < 12, "Leadership",
+ ? ? ifelse(item < 15, "Task Sig.", "Hostility"))))
R> for (i in c(1, 2, 4, 5)) lql[[i]] <- factor(lql[[i]])
R> lql$dichot <- factor(ifelse(lql$fivelev < 4, 0, 1))

R> (fm1 <- lmer(dichot ~ 0 + itype + (1 | subj) + (1 | COMPID) +
+ ? ? (1 | item), lql, binomial))
Generalized linear mixed model fit using Laplace
Formula: dichot ~ 0 + itype + (1 | subj) + (1 | COMPID) + (1 | item)
? Data: lql
?Family: binomial(logit link)
? AIC ? BIC logLik deviance
?40722 40773 -20355 ? ?40710
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?subj ? (Intercept) 2.30528 1.51831
?COMPID (Intercept) 0.25449 0.50447
?item ? (Intercept) 0.37700 0.61400
number of obs: 38798, groups: subj, 2042; COMPID, 49; item, 19

Estimated scale (compare to ?1 ) ?0.9386558 ?#<---------------------------- this!

sqrt(sum(c(fm1 at resid, fm1 at u)^2)/length(fm1 at resid))

Fixed effects:
? ? ? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
itypeHostility ? ?1.6721 ? ? 0.2883 ? 5.801 6.6e-09
itypeLeadership ?-0.4921 ? ? 0.2036 -2.417 ? ?0.0157
itypeTask Sig. ? -0.1308 ? ? 0.3654 -0.358 ? ?0.7203
Correlation of Fixed Effects:
? ? ? ? ? ?itypHs itypLd
itypeLdrshp 0.117
itypeTskSg. 0.066 0.093

My actual output is:

fm1

Generalized linear mixed model fit by the Laplace approximation
Formula: dichot ~ 0 + itype + (1 | subj) + (1 | COMPID) + (1 | item)
? Data: lql
? AIC ? BIC logLik deviance
?40722 40773 -20355 ? ?40710
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?subj ? (Intercept) 2.30573 ?1.51846
?COMPID (Intercept) 0.25416 ?0.50415
?item ? (Intercept) 0.37488 ?0.61227
Number of obs: 38798, groups: subj, 2042; COMPID, 49; item, 19

Fixed effects:
? ? ? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
itypeHostility ? ?1.6774 ? ? 0.2875 ? 5.834 ?5.4e-09 ***
itypeLeadership ?-0.4928 ? ? 0.2031 ?-2.426 ? 0.0153 *
itypeTask Sig. ? -0.1362 ? ? 0.3644 ?-0.374 ? 0.7086
---
Signif. codes: ?0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Correlation of Fixed Effects:
? ? ? ? ? ?itypHs itypLd
itypeLdrshp 0.118
itypeTskSg. 0.066 ?0.093

sum(residuals(fm1, type="pearson")^2)/(nrow(lql)-3-3)

[1] 0.8399116

deviance(fm1)/(nrow(lql)-3-3)

? ? ?ML
1.049437

As you can see, I can't figure out the estimated scale parameter. How can I get it? Why was this information removed from the output in newer versions of the function?

Thanks in advance.




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