[R] lme and aov
Gang Chen
gangchen at mail.nih.gov
Fri Aug 3 22:22:09 CEST 2007
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: r-help-bounces at stat.math.ethz.ch
>> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Gang Chen
>> Sent: Friday, August 03, 2007 4:01 PM
>> To: Peter Dalgaard
>> Cc: r-help at 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
>>
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