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!

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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