On Thu, Mar 5, 2009 at 8:31 AM, kayj <kjaj...@yahoo.com> wrote: > > Hi All, > > > I have about one hundred patients and all the patients had their glucose > measured on three different days. The days are all the same for all he > patients. So I have three measurement for each patient . I want to know > whether the day when the glucose was measured has an effect on the > measurements. I was thinking to use a single factor analysis of variance but > I am not sure how to do it in R.
Two options for one-way ANOVA are aov(response ~ factor), or lm(response ~ factor) followed by anova(lm.object). But....this would probably be a bad idea. I recommend Pinheiro and Bates (2000), where there are many examples of modeling effects within subjects over time. Here's an example with a continuous time effect library(nlme) f1 <- lme(distance~age, data=Orthodont, random= ~1 + age|Subject, weights=varPower()) plot(f1, distance~fitted(.)|Subject, abline=c(0,1)) intervals(f1) hth, Kingsford Jones >any other suggestion on dealing with this > problem is welcome. > > Thanks, > > -- > View this message in context: > http://www.nabble.com/ANOVA-tp22353919p22353919.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org 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. > ______________________________________________ R-help@r-project.org 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.
