[R] lme and aov

[Gang Chen]

> This looks odd.  It is a standard split-plot layout, right? 3  
> groups of 13 subjects, each measured with two kinds of Rsp = 3x13x2  
> = 78 observations.

Yes, that is right.

>
> In that case you shouldn't see the same effect allocated to  
> multiple error strata. I suspect you forgot to declare Subj as factor.


This is exactly the problem I had: Model$Subj was not a factor! Now  
they converge. A lesson well learned.

Thanks a lot for the help,
Gang


On Aug 3, 2007, at 4:53 PM, Peter Dalgaard wrote:

> Gang Chen wrote:
>> 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