Douglas Bates <bates <at> stat.wisc.edu> writes: > If you want to examine the three means then you should fit the model as > lmer(rcl ~ time - 1 + (1 | subj), fr) >
True, but for the notorious "error bars" in plots that reviewers always request the 0.35 is probable more relevant than the 1.87. Which I think is justified in this case, but in most non-orthogonal designs with three or more factors, where we have a mixture of between/withing subject, there is no clear solution. What to do when required to produce "error-bars" that reasonably mirror p-values? It's easier with British Journals in the medical field that often have statistical professionals as reviewers, but many American Journals with their amateur physician/statisticians (why no t-test on raw data?) drive me nuts. Dieter #------------- library(lme4) recall <- c(10, 13, 13, 6, 8, 8, 11, 14, 14, 22, 23, 25, 16, 18, 20, 15, 17, 17, 1, 1, 4, 12, 15, 17, 9, 12, 12, 8, 9, 12) fr <- data.frame(rcl = recall, time = factor(rep(c(1, 2, 5), 10)), subj = factor(rep(1:10, each = 3))) fr.lmer <- lmer(rcl ~ time -1 +(1 | subj), fr) summary(fr.lmer) fr.lmer <- lmer(rcl ~ time +(1 | subj), fr) summary(fr.lmer) ------------------ Fixed effects: Estimate Std. Error t value time1 11.000 1.879 5.853 time2 13.000 1.879 6.918 time5 14.200 1.879 7.556 Fixed effects: Estimate Std. Error t value (Intercept) 11.0000 1.8793 5.853 time2 2.0000 0.3507 5.703 time5 3.2000 0.3507 9.125 ______________________________________________ R-help@r-project.org 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.