On 5/14/07, Iasonas Lamprianou <[EMAIL PROTECTED]> wrote: > Does anyone know if the lmer function of lme4 works fine for unbalanced > designs? I have the examination results of 1000 pupils on three subjects, one > score every term. So, I have three scores for English (one for every term), > three scores for maths etc. However, not everybody was examined in maths, not > everybody was examined in English etc, but everybody was in effect examined > on four subjects. I also have information abouit the school. Would this model > hive the right results for the variance components? > > mod_3_f <- lmer(SCORE ~ GENDER + (1 |ID ) + (1 | TERM) + (1 | SUBJECT) , > Dataset) > > Linear mixed-effects model fit by REML > Formula: SCORE ~ GENDER + (1 | ID) + (1 | TERM) + (1 | SUBJECT) > Data: Dataset > AIC BIC logLik MLdeviance REMLdeviance > 247882 247926 -123936 247871 247872 > Random effects: > Groups Name Variance Std.Dev. > ID (Intercept) 5.97288 2.44395 > TERM (Intercept) 5.10307 2.25900 > SUBJECT (Intercept) 0.25943 0.50934 > Residual 4.41673 2.10160 > number of obs: 53978, groups: ID, 5695; TERM, 4; SUBJECT, 4 > Fixed effects: > Estimate Std. Error t value > (Intercept) 14.30352 1.15870 12.34 > GENDER[T.Male] -1.01776 0.06885 -14.78 > Correlation of Fixed Effects: > Warning in x$symbolic.cor : $ operator not defined for this S4 class, > returning NULL
What version of the lme4 package are you using? (Use sessionInfo() to check.) I think the bug that causes that warning has been fixed in the most recent version. > (Intr) > GENDER[T.M] -0.023 > How do I interpert the results? Do you really want to treat SUBJECT as a random effect? I think it would be more common to treat it as a fixed effect. If I understand you correctly there are only two levels of SUBJECT and these are repeatable levels. If that is the case one could model SUBJECT as a fixed effect or consider its interaction within student with the term (SUBJECT|ID). It would make sense to regard the pair of responses in maths and English for each student in each term as a multivariate response but, at present, that model cannot be fit with lmer. I would also question whether you want the TERM to be modeled with a random effect. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
