contrasts among simple effects

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_repeat
ed_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