Dear R-users,

I'm running a lmer model using the lme4 package. My dependent variable is
dichotomous and I'm using the "binomial" family. The results
are slightly different from the HLM results based on a Bernoulli
distribution. Please, see the results below:

Level 1 info:  Size: 129006      Mean: 0.7082 (dichotomous variable 0/1)

Level2 info:   Size:  384



*HLM model:*

Distribution at Level-1: Bernoulli

Level-1 Model

                Prob(Y=1|B) = P

                log[P/(1-P)] = B0



Level-2 Model

                B0 = G00 + U0


*HLM results:*

Random effects:

Groups Name          Variance Std.Dev.

schid  (Intercept)     0.17727  0.42104



Fixed effects:

                          Estimate Std. Error    z value Pr(>|z|)

(Intercept)          1.001561     0.023039  43.473   0.000





*R model:*

lmer(measurebi_general ~ 1 + (1 | schid), data=data_valid_general,
family=binomial)



*R results:*

   AIC    BIC logLik deviance

 153195 153214 -76595   153191



Random effects:

Groups Name          Variance Std.Dev.

schid  (Intercept)     0.18007  0.42434



Fixed effects:

                          Estimate Std. Error z value Pr(>|z|)

(Intercept)          1.0071     0.0232   43.41   <2e-16 ***



Is there anyway that I can adapt my R model so that the R results are the
same as the HLM results?


I'm using the lme4 for linear data and the R results are exactly the same as
the ones produced by HLM. It is very important for me to have both results
the same because I'll be discussing the results with researchers who use
exclusively the HLM software.


Thank you,


Luana Marotta

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