On 3/30/06, Prof Brian Ripley <[EMAIL PROTECTED]> wrote: > I think you want print or summary rather than anova. anova() is not very > useful for aov() models even without error strata.
That's sort of better. summary(aov(time ~ drink + Error(video), data = df)); gives me: Error: video Df Sum Sq Mean Sq F value Pr(>F) Residuals 2 160 80 Error: Within Df Sum Sq Mean Sq F value Pr(>F) drink 1 240.000 240.000 44.211 1.313e-08 *** Residuals 56 304.000 5.429 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ...what I'm really looking for is something akin to the output from SAS, which is: Source DF Anova SS Mean Square F Value Pr > F DRINK 1 240.000 240.000 12.00 0.0742 VIDEO 2 160.000 80.000 4.00 0.2000 > I didn't follow how the videos were chosen. Random effects apply when the > 'treatments' were chosen from a large population (which might apply if > each subject watched (on separate occasions) three videos chosen randomly > from a larger pool), and if the interest is in the variability of the > response over videos in the pool. If subjects were observed more than > once then I suspect you most likely want a random effect for subjects. This problem comes directly from the final for my Experimental Stats class, which is why it feels a little odd. The videos were randomly selected from a library. Subjects watched one of the three videos, drank one of the two drinks, and completed the tasks. There were no repeated measures, so we can't block on subjects. The hypothesis test, according to SAS, treats DRINK*VIDEO as an error term. Setting aside whether this is the right analysis, how can I replicate this analysis in R? -- Chris ______________________________________________ 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