Have you seen Pinheiro and Bates (2000) that lays out the nlme package? It is very helpful. Hank On Apr 14, 2007, at 8:02 PM, Kyle. wrote:
> You probably can do this with lme function, but I don't know that for > sure. "aov" (included in the "stats" package), with a call to the > "Error" function how I generally analyze data obtained from a > repeated measure design. For a very good description of how Error > and aov work together, you should read Baron's “Notes on the use of R > for psychology experiments and questionnaires” (follow the link > below) The section beginning on page 36 addresses your question. > Generally speaking, the aov function expects you to describe your > model formula similarly to how you would with a call to the "lm" > function. For example, Y~X1*X2 describes a two way ANOVA--your > dependent variable Y modeled as explanatory variables X1 and X2, > including the interaction term: > model.aov<-aov(Y~X1*X2, data=DataFile) > > If X1 and X2 are within-subject variables, then the above formula > would be written as follows: > > model.aov<-aov(Y~X1*X2+Error(Subject/X1*X2),data=DataFile) > > The call "summary(model.aov)" will display a summary of the model. > Depending on your experience working with model formulae in R, there > are several steps in the repeated-measure ANOVA procedure that can > come back to haunt you, if you're not careful (e.g., checking the > assumption of sphericity, normally distributed errors, homogeneity of > variance, etc., etc.), so make sure you're covered on these before > you believe your results. > > Here's a link to the document I referred to: http://cran.r- > project.org/doc/contrib/Baron-rpsych.pdf > > > > > Kyle H. Ambert > Graduate Student, Dept. Behavioral Neuroscience > Oregon Health & Science University > [EMAIL PROTECTED] > > > > > > > On Apr 8, 2007, at 7:55 PM, Scott Norton wrote: > >> Hi, >> I have what I believe is a repeated-measures dataset that I'm >> trying to analyze using lme(). This is *not* homework, but an >> exercise in my trying to self-teach myself repeated-measure ANOVA >> for other *real* datasets that I have and that are extremely >> similar to the following design. >> >> I'm fairly sure the dataset described below would work with lme() >> -- but it'd be great if anybody can confirm that after I describe >> the dataset below) >> >> The study involves measuring the effect of a drug on blood >> pressure. There were 16 patients in all and 6 replicate measures >> per patient of their blood pressure on one week (one measure per >> day). Two weeks later, a drug was introduced to 8 randomly selected >> patients in such a way that I had equal representation of the 4 age >> groups among the two treatment groups. Then, another two weeks >> later, 6 replicate measures per patient (per day) of blood pressure >> was retaken. So each patient had 12 total measures whether they >> were in the treatment group or in the control group (6 reads (R1- >> R6) in the baseline-week and another 6 reads (R1-R6) in the post- >> treatment week). >> >> So, >> Background: 16 patients >> Response measure: Blood pressure >> Fixed Factor: 4 Age groups >> Fixed Factor: Drug vs. NoDrug >> Random factor: Day of the read (i.e. 6 replicate reads (R1-R6) at >> the baseline time, and 6 replicate reads (R1-R6) after the drug has >> had time to take effect) >> Random Factor: Subjects 1-16 >> >> Patient AgeGroup BP(Blood Pressure) Read (replicate >> reads) Pre/PostTreatmentWeek Group >> 1 20-29 83 >> R1 >> pre Treat >> 2 20-29 81 >> R1 >> pre Control >> 3 20-29 74 >> R1 >> pre Treat >> 4 20-29 85 >> R1 >> pre Control >> 5 30-39 82 >> R1 >> pre Treat >> > > > > > > >> 3 20-29 74 >> R2 >> pre Treat >> > > > > > > >> 1 20-29 83 >> R1 >> post Treat >> 2 20-29 82 >> R1 >> post Control >> 3 20-29 86 >> R1 >> post Treat >> 4 20-29 84 >> R1 >> post Control >> > > > > > > >> >> I'm trying to do an analysis of variance to decide whether there is >> a measurable change in blood pressure between the Treat and Control >> groups. >> >> Another issue is that some of the 16 patients didn't get all 6 >> replicate reads in their pre/post treatment weeks, so I need to >> include the na.omit function. >> >> What I think I'm having the most trouble with is the repeated reads >> (R1 through R6) in the pre/post treatment weeks. I'm fairly sure >> this is a random variable -- their order or identify (R1 in pre- >> treatment week has no relation to R1 in the post-treatment week, >> etc). By placing Read as a random variable, am I covering myself >> there? >> If I execute: >> >>> summary(lme(BP ~ Group, random = ~ 1 | Patient, data = bloodpress, >>> na.action=na.omit)) >> >> I get a result, but I'm not sure it's correct -- do I need to tell >> the model about the Read factor (R1-R6 in pre/post weeks)? >> >> I'm really trying to set the right form of the lme() function call >> to decide >> 1) if there is a statistical difference between the Treat/Control >> groups and, >> 2) if one takes into account AgeGroup, is there a statistical >> difference between Treat/Control Groups, and finally >> 3) if I don't see a statistical difference, can someone recommend >> an R function that might solve the supplemental question, "given >> the noise in day-to-day blood pressure reads, and given that I >> wanted to have enough statistical power to observe a say, a 5% >> benefit in blood pressure, how many additional reads or patients I >> would need." >> >> Basically, is lme() the proper function, and can someone offer any >> pointers on what my call to this function should look like to make >> the above to determinations? >> >> Thanks! >> -Scott >> >> ______________________________________________ >> R-help@stat.math.ethz.ch mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting- >> guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. Dr. Hank Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/~stevenmh/ http://www.muohio.edu/ecology/ http://www.muohio.edu/botany/ "E Pluribus Unum" ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.