Thanks a lot for clarification! I just started to learn programming in R for a week, and wanted to try a simple mixed design of balanced ANOVA with a between-subject factor (Grp) and a within-subject factor (Rsp), but I'm not sure whether I'm modeling the data correctly with either of the command lines.
Here is the result. Any help would be highly appreciated. > fit.lme <- lme(Beta ~ Grp*Rsp, random = ~1|Subj, Model); > summary(fit.lme) Linear mixed-effects model fit by REML Data: Model AIC BIC logLik 233.732 251.9454 -108.8660 Random effects: Formula: ~1 | Subj (Intercept) Residual StdDev: 1.800246 0.3779612 Fixed effects: Beta ~ Grp * Rsp Value Std.Error DF t-value p-value (Intercept) 1.1551502 0.5101839 36 2.2641837 0.0297 GrpB -1.1561248 0.7215090 36 -1.6023706 0.1178 GrpC -1.2345321 0.7215090 36 -1.7110417 0.0957 RspB -0.0563077 0.1482486 36 -0.3798196 0.7063 GrpB:RspB -0.3739339 0.2096551 36 -1.7835665 0.0829 GrpC:RspB 0.3452539 0.2096551 36 1.6467705 0.1083 Correlation: (Intr) GrpB GrpC RspB GrB:RB GrpB -0.707 GrpC -0.707 0.500 RspB -0.145 0.103 0.103 GrpB:RspB 0.103 -0.145 -0.073 -0.707 GrpC:RspB 0.103 -0.073 -0.145 -0.707 0.500 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.72266114 -0.41242552 0.02994094 0.41348767 1.72323563 Number of Observations: 78 Number of Groups: 39 > fit.aov <- aov(Beta ~ Rsp*Grp+Error(Subj/Rsp)+Grp, Model); > fit.aov Call: aov(formula = Beta ~ Rsp * Grp + Error(Subj/Rsp) + Grp, data = Model) Grand Mean: 0.3253307 Stratum 1: Subj Terms: Grp Sum of Squares 5.191404 Deg. of Freedom 1 1 out of 2 effects not estimable Estimated effects are balanced Stratum 2: Subj:Rsp Terms: Rsp Sum of Squares 7.060585e-05 Deg. of Freedom 1 2 out of 3 effects not estimable Estimated effects are balanced Stratum 3: Within Terms: Rsp Grp Rsp:Grp Residuals Sum of Squares 0.33428 36.96518 1.50105 227.49594 Deg. of Freedom 1 2 2 70 Residual standard error: 1.802760 Estimated effects may be unbalanced Thanks, Gang On Aug 3, 2007, at 4:09 PM, Doran, Harold wrote: > Gang: > > I think what Peter is asking for is for you to put some of your output > in an email. If the values of the fixed effects are the same across > models, but the F-tests are different, then there is a whole other > thread we will point you to for an explanation. (I don't presume to > speak for other people, btw, and I'm happy to stand corrected) > >> -----Original Message----- >> From: [EMAIL PROTECTED] >> [mailto:[EMAIL PROTECTED] On Behalf Of Gang Chen >> Sent: Friday, August 03, 2007 4:01 PM >> To: Peter Dalgaard >> Cc: r-help@stat.math.ethz.ch >> Subject: Re: [R] lme and aov >> >> Thanks for the response! >> >> It is indeed a balanced design. The results are different in >> the sense all the F tests for main effects are not the same. >> Do you mean that a random interaction is modeled in the aov >> command? If so, what would be an equivalent command of aov to >> the one with lme? >> >> Thanks, >> Gang >> >> On Aug 3, 2007, at 3:52 PM, Peter Dalgaard wrote: >> >>> Gang Chen wrote: >>>> I have a mixed balanced ANOVA design with a >> between-subject factor >>>> (Grp) and a within-subject factor (Rsp). When I tried the >> following >>>> two commands which I thought are equivalent, >>>> >>>>> fit.lme <- lme(Beta ~ Grp*Rsp, random = ~1|Subj, Model); > >>>> fit.aov <- aov(Beta ~ Rsp*Grp+Error(Subj/Rsp)+Grp, Model); >>>> >>>> I got totally different results. What did I do wrong? >>>> >>>> >>> Except for not telling us what your data are and what you mean by >>> "totally different"? >>> >>> One model has a random interaction between Subj and Rsp, the other >>> does not. This may make a difference, unless the >> interaction term is >>> aliased with the residual error. >>> >>> If your data are unbalanced, aov is not guaranteed to give >> meaningful >>> results. >>> >>> -pd >> >> ______________________________________________ >> R-help@stat.math.ethz.ch 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. >> ______________________________________________ R-help@stat.math.ethz.ch 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.