[R-sig-ME] contrasts among simple effects

[Thierry Onkelinx]

Dear James,

I think that you need to specify the order of the data as well. corAR1(form
= ~time|person). Otherwise the order of the observations as present in the
data is used.

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

2015-10-14 18:46 GMT+02:00 James Henson <jfhenson1 op gmail.com>:

> Greetings R Community
>
> Apologize for previously sending a csv file.
>
> My goal is to make orthogonal contrasts among simple effects in analysis of
> repeated measures data.  The SAS publication, on page 1224, shows how to
> make this type of contrasts in SAS.  But, my search of books about repeated
> measures analysis using R, and on-line has not yielded a methodology.
> Hopefully, someone can direct me to a book or publication that will show me
> a methodology.
>
> Statistical Analysis of Repeated Measures Data Using SAS Procedures
>
>
> http://cslras.pbworks.com/f/littell_j_anim_sci_76_4_analysis_of_repeated_measures_using_sas.pdf
>
>
>
> Attached is a txt data file (file name = heart_rate.txt).  My code for the
> repeated measures analysis is below.
>
> library("nlme")
>
> # with AR1 variance/covariance structure, with ordered statement
>
> heartRate$time <- factor(heartRate$time)
>
> model2a <- lme(HR ~ drug*ordered(time), random =~1|person, correlation
> =corAR1(, form=~1|person), data = heartRate)
>
> summary(model2a)
>
> anova(model2a)
>
> Making a new variable ‘simple’ that merges the variables drug and time will
> enable me to make orthogonal contrasts among the simple effects.  But, when
> using the variable ‘simple’ as the independent variable, the data will no
> longer be fitted to the AR1 variance/covariance structure.
>
> Thanks.
>
> Best regards,
>
> James F.Henson
>
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