Jacques, Thanks for the reply. I am not using lme because I dont have the time to understand how it works and I have a balanced design, so typcial linear modelling in aov should be sufficient for my purposes. Down the road I plan to learn lme, but I'm not there yet. So any suggestions with respect to aov would be greatly appreciated.
Steve -----Original Message----- From: Jacques Veslot [mailto:[EMAIL PROTECTED] Sent: Friday, April 21, 2006 11:58 AM To: Steven Lacey Cc: [email protected] Subject: Re: [R] aov contrasts residual error calculation why not using lme() ? first, you need transform data: dat2 <- as.data.frame(lapply(subset(dat, sel=-c(A,B,C)), rep, 3)) dat2$y <- unlist(subset(dat, sel=c(A,B,C)), F, F) dat2$cond <- factor(rep(c("A","B","C"), each=nrow(dat))) dat2$inter <- factor(dat2$map):factor(dat2$cond) lme1 <- lme(fixed = y ~ mapping + cond + inter + other fixed effects, random = ~ 1 |subj, data=dat2, contrast=list(inter=poly(nlevels(dat2$inter)[,1:4])) Steven Lacey a écrit : > Hi, > > I am using aov with an Error component to model some repeated measures > data. By repeated measures I mean the data look something like this... > > subj A B C > 1 4 11 15 > 2 3 12 17 > 3 5 9 14 > 4 6 10 18 > > For each subject I have 3 observations, one in each of three > conditions (A, B, C). I want to test the following contrast (1, 0, > -1). One solution is to apply the contrast weights at the subject > level explicitly and then call t.test on the difference scores. > However, I am looking for a more robust solution as I my actual design > has more within-subjects factors and one or more between subjects > factors. > > A better solution is to specify the contrast in an argument to aov. > The estimated difference of the contrast is the same as that in the > paired t-test, but the residual df are double. While not what I > expected, it follows from the documentation, which explicitly states > that these contrasts are not to be used for any error term. Even > though I specify 1 contrast, there are 2 df for a 3 level factor, and > I suspect internally the error term is calculated by pooling across > multiple contrasts. > > While very useful, I am wondering if there is way to get aov to > calculate the residual error term only based on the specified > contrasts (i.e., not assume homogeneity of variance and sphericity) > for that strata? > > If not, I could calculate them directly using model.matrix, but I've > never done that. If that is the preferred solution, I'd also > appreciate coding suggestions to do it efficiently. > > > How would I do the same thing with a two factor anova where one factor > is within-subjects and one is between... > Condition > Mapping Subject A B C > 1 1 4 11 15 > 1 2 > 1 3 > 1 4 > 1 5 > 1 6 > 1 7 > 1 8 > 2 9 > 2 10 > > Mapping is a between-subject factor. Condition is a within-subject > factor. There are 5 levels of mapping, 8 subjects nested in each level > of mapping. For each of the 40 combinations of mapping and subject > there are 3 observations, one in each level of the condition factor. > > I want to estimate the pooled error associated with the following set > of 4 orthogonal contrasts: > > condition.L:mapping.L > condition.L:mapping.Q > condition.L:mapping.C > condition.L:mapping^4 > > What is the best way to do this? One way is to estimate the linear > contrast for condition for each subject, create a 40 row matrix where > the measure for each combination of mapping and subject is the linear > contrast on condition. If I pass this dataframe to aov, the mse it > returns is the value I am looking for. > > If possible, I would like to obtain the estimate without collapsing > the dataframe, but am not sure how to proceed. Suggestions? > > Thanks, > Steve > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > -- ------------------------------------------------------------------- [EMAIL PROTECTED] CNRS UMR 8090 - http://www-good.ibl.fr Génomique et physiologie moléculaire des maladies métaboliques I.B.L 2eme etage - 1 rue du Pr Calmette, B.P.245, 59019 Lille Cedex Tel : 33 (0)3.20.87.10.44 Fax : 33 (0)3.20.87.10.31 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
