Dear all, I am new to R and would like to seek your advice on a project I am doing. I am analyzing some administrative data to learn about how each county in IL stand with each other in the number of children placed in public foster care in a year. For each county, we have the children placed in foster care (fp), its total child population (childn), and a bunch of county-level attributes from the census data. I decided to adopt the poisson model under the lme4 package in R to run a mixed effect poisson model. Here is the syntax I use:
fit2=lmer(fp~1+offset(log(childn))+(1|county),family=poisson,data=ildata,reml=TRUE) Here are the results:
fit2
Generalized linear mixed model fit by the Laplace approximation Formula: fp ~ offset(log(childn)) + (1 | county) Data: ildata AIC BIC logLik deviance 334.1 339.3 -165 330.1 Random effects: Groups Name Variance Std.Dev. county (Intercept) 0.53857 0.73388 Number of obs: 101, groups: county, 101 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -6.25386 0.07988 -78.29 <2e-16 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 To get the predicted result for each county, I need to get the random effect (residuals) for each county as well as their variances. However, the ranef command only produce the random effect without giving the confidence interval / variance. I would be glad if you can let me know how to produce the confidence interval of these random effects so I can calculate their confidence intervals.
ranef(fit2)
$county (Intercept) 4001 0.612613842 4003 0.066916182 4005 1.293875058 My last question is whether the random residuals generated in R are empirically bayesian estimates, as in HLM. Thanks a lot , Lijun Lijun Chen Senior Research Specialist Chapin Hall at the University of Chicago 1313 E. 60th St., Chicago, IL 60637 V: 773-256-5140 F: 773-256-5340 http://www.chapinhall.org