Hi All,
I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA &url=http%3A%2F%2Fwww.ualberta.ca%2F~baayen%2Fpublications%2FbaayenDavidsonB ates.pdf&ei=FhqTUoXuJKKV7Abds4GYBA&usg=AFQjCNFst7GT7mBX7w9lXItJTtELJSKWJg&si g2=KGA5MHxOvEGwDxf-Gcqi6g&bvm> R.H. et al 2008) Here, dT_purs is the response variable, T and Z are the fixed effects, and subject is the random effect. Random and fixed effects are crossed.: mod0 <- lmer(dT_purs ~ T + Z + (1|subject), data = x) mod1 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject), data = x) mod2 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod3 <- lmer(dT_purs ~ T * Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod4 <- lmer(dT_purs ~ T * Z + (1| subject), data = x) anova(mod0, mod1,mod2, mod3, mod4) Data: x Models: mod0: dT_purs ~ T + Z + (1 | subject) mod4: dT_purs ~ T * Z + (1 | subject ) mod1: dT_purs ~ T + Z + (1 + T| subject) mod2: dT_purs ~ T + Z + (1 + T| subject ) + (1 + Z | subject) mod3: dT_purs ~ T * Z + (1 + T| subject) + (1 + Z | subject) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) mod0 5 -689.81 -669.46 349.91 -699.81 mod4 6 -689.57 -665.14 350.78 -701.57 1.7532 1 0.185473 mod1 7 -689.12 -660.62 351.56 -703.12 1.5504 1 0.213070 mod2 10 -695.67 -654.97 357.84 -715.67 12.5563 3 0.005701 ** mod3 11 -695.83 -651.05 358.92 -717.83 2.1580 1 0.141825 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 It turns out that mod2 has the right level of complexity for this dataset. However when I looked at its summary, I got a correlation of -0.87 for the random effects relative to the T effect and -1 for the random effects relatively to the Z. summary(mod2) Linear mixed model fit by maximum likelihood ['lmerMod'] Formula: dT_purs ~T + Z + (1 + T | subject) + (1 + Z | subject) Data: x AIC BIC logLik deviance -695.6729 -654.9655 357.8364 -715.6729 Random effects: Groups Name Variance Std.Dev. Corr subject (Intercept) 0.0032063 0.05662 T 0.0117204 0.10826 -0.87 subject.1 (Intercept) 0.0005673 0.02382 Z 0.0025859 0.05085 1.00 Residual 0.0104551 0.10225 Number of obs: 433, groups: soggetto, 7 Fixed effects: Estimate Std. Error t value (Intercept) 0.02489 0.03833 0.650 T 0.52010 0.05905 8.808 Z -0.09019 0.02199 -4.101 Correlation of Fixed Effects: (Intr) tempo T -0.901 Z 0.218 -0.026 If I understand correctly what the correlation parameters reported in the table are, the correlation of 1 means that, for the Z effects the random intercept is perfectly collinear with the random slope. Thus, we fit the wrong model. A random intercept only model would have sufficed. Am I correct? If so, should I take mod1 (mod1 <- dT_purs ~ T + Z + (1 + T | subject ) instead of mod2 to fit my data? Why are these results contradictory? Finally is a correlation value of -0.87 a too high or an acceptable value ? Thanks for help me in advance! Best Benedetta --- Benedetta Cesqui, Ph.D. Laboratory of Neuromotor Physiology IRCCS Fondazione Santa Lucia Via Ardeatina 306 00179 Rome, Italy tel: (+39) 06-51501485 fax:(+39) 06-51501482 E_mail: b.ces...@hsantalucia.it [[alternative HTML version deleted]] ______________________________________________ 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.
