Preface to First Edition - lib

ANALYSIS USING R 2211.5m No >6m TAU 29 1 5 NA2.5m Yes >6m BtheB 32 2 5 173.5m Yes 6m TAU 29 1 8 NA2.8m Yes >6m BtheB 32 2 8 203.8m Yes library("lme4")R> BtheB_lmer1 BtheB_lmer2 anova(BtheB_lmer1, BtheB_lmer2)Data: BtheB_longModels:BtheB_lmer1: bdi ~ bdi.pre + time + treatment + drug + length +BtheB_lmer1: (1 | subject)BtheB_lmer2: bdi ~ bdi.pre + time + treatment + drug + length +BtheB_lmer2: (time | subject)Df AIC BIC logLik Chisq Chi DfBtheB_lmer1 8 1887.49 1916.57 -935.75BtheB_lmer2 10 1891.04 1927.39 -935.52 0.4542 2Pr(>Chisq)BtheB_lmer1BtheB_lmer2 0.7969The log-likelihood test indicates that the simpler random intercept modelis adequate for these data. More information about the fitted random interceptmodel can be extracted from object BtheB_lmer1 using summary by theR code in Figure 12.2. We see that the regression coefficients for time andthe Beck Depression Inventory II values measured at baseline (bdi.pre) arehighly significant, but there is no evidence that the coefficients for the otherthree covariates differ from zero. In particular, there is no clear evidence of atreatment effect.The summary method for lmer objects doesn’t print p-values for Gaussianmixed models because the degrees of freedom of the t reference distribution arenot obvious. However, one can rely on the asymptotic normal distribution for© 2010 by Taylor and Francis Group, LLC