I never used the groupedData structure, precisely because I found it
confusing, but I think:
1. group is *not* a (random) grouping variable; it's a fixed effect covariate.
2. so I believe your groupedData call should be:
GD<- groupedData(value~time|SS,data=data1,outer = group)
Of course, as you did not give us your data in a convenient form, I
can't check. Please let us know if this is wrong, however, as I don't
want to mislead others down the primrose path.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Aug 24, 2016 at 3:46 PM, John Sorkin
<jsor...@grecc.umaryland.edu> wrote:
> I apologize for sending this message again. The last time I sent it, the
> subject line was not correct. I have corrected the subject line.
>
> I am trying to run a repeated measures analysis of data in which each subject
> (identified by SS) has 3 observations at three different times (0, 3, and 6).
> There are two groups of subjects (identified by group). I want to know if the
> response differs in the two groups. I have tried to used lme. Lme tell me if
> there is a time effect, but does not tell me if there is a group effect. Once
> I get this to work I will want to know if there is a significant group*time
> effect. Can someone tell me how to get an estimate for group. Once I get
> that, I believe getting an estimate for group*time should be straight
> forward. The code I have tired to use follows.
> Thank you,
> John
>
>> # This is my data
>> data1
> SS group time value baseline
> 1 1 Cont 0 9.000000 9.000000
> 2 2 Cont 0 3.000000 3.000000
> 3 3 Cont 0 8.000000 8.000000
> 4 4 Inte 0 5.690702 5.690702
> 5 5 Inte 0 7.409493 7.409493
> 6 6 Inte 0 7.428018 7.428018
> 7 1 Cont 3 13.713148 9.000000
> 8 2 Cont 3 9.841107 3.000000
> 9 3 Cont 3 12.843236 8.000000
> 10 4 Inte 3 9.300899 5.690702
> 11 5 Inte 3 10.936389 7.409493
> 12 6 Inte 3 12.358499 7.428018
> 13 1 Cont 6 18.952390 9.000000
> 14 2 Cont 6 15.091527 3.000000
> 15 3 Cont 6 17.578812 8.000000
> 16 4 Inte 6 12.325499 5.690702
> 17 5 Inte 6 15.486513 7.409493
> 18 6 Inte 6 18.284965 7.428018
>> # Create a grouped data object. SS identifies each subject
>> # group indentifies group, intervention or control.
>> GD<- groupedData(value~time|SS/group,data=data1,FUN=mean)
>> # Fit the model.
>> fit1 <- lme(GD)
>> cat("The results give information about time, but does not say if the gruops
>> are different\n")
> The results give information about time, but does not say if the gruops are
> different
>> summary(fit1)
> Linear mixed-effects model fit by REML
> Data: GD
> AIC BIC logLik
> 74.59447 81.54777 -28.29724
>
> Random effects:
> Formula: ~time | SS
> Structure: General positive-definite
> StdDev Corr
> (Intercept) 1.3875111 (Intr)
> time 0.2208046 -0.243
>
> Formula: ~time | group %in% SS
> Structure: General positive-definite
> StdDev Corr
> (Intercept) 1.3875115 (Intr)
> time 0.2208051 -0.243
> Residual 0.3800788
>
> Fixed effects: value ~ time
> Value Std.Error DF t-value p-value
> (Intercept) 6.747442 0.8135067 11 8.294268 0
> time 1.588653 0.1326242 11 11.978601 0
> Correlation:
> (Intr)
> time -0.268
>
> Standardized Within-Group Residuals:
> Min Q1 Med Q3 Max
> -1.11412947 -0.44986535 0.08034174 0.34615610 1.29943887
>
> Number of Observations: 18
> Number of Groups:
> SS group %in% SS
> 6 6
>
>
>
>>
> John David Sorkin M.D., Ph.D.
> Professor of Medicine
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology and
> Geriatric Medicine
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>
> Confidentiality Statement:
> This email message, including any attachments, is for ...{{dropped:12}}
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