[R] lme to determine if there is a group effect

[Thierry Onkelinx]

Dear John,

lme() not longer requires a GroupedData object. You can directly use a
data.frame which is easier to specify different models.

You want something like

lme(value ~ time * group, random = ~ time|SS, data = data1)

PS Note that the R-Sig-mixedmodels is more suited for this kind of question.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-08-25 0:46 GMT+02:00 John Sorkin <jsorkin op grecc.umaryland.edu>:

> 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:16}}