tag:blogger.com,1999:blog-6710487119650146215.post2593087957678846202..comments2017-05-04T21:56:30.004-07:00Comments on R Tutorial Series: R Tutorial Series: Two-Way ANOVA with Interactions and Simple Main EffectsJohnhttp://www.blogger.com/profile/05331039307550313006noreply@blogger.comBlogger10125tag:blogger.com,1999:blog-6710487119650146215.post-33715146959908138572015-02-19T10:35:41.403-07:002015-02-19T10:35:41.403-07:00For anyone else who's finding this years later...For anyone else who's finding this years later through Google searches, there's a new paper covering different approaches to post hoc analysis of factorial / two-way / between groups ANOVA.<br /><br />http://cran.r-project.org/web/packages/phia/vignettes/phia.pdfAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-13794532214904276782014-09-19T11:15:04.294-07:002014-09-19T11:15:04.294-07:00Hi John,
Great tutorial. Is there any way to cond...Hi John,<br /><br />Great tutorial. Is there any way to conduct t-tests of your contrasts (e.g., f vs m for medical; f vs m for mental; f vs m for physical) using the pairwise.t.test function so that you can adjust the p-values using Holm, FDR, etc corrections? <br /><br />SAJnoreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-25715175728986493372013-08-16T00:56:42.839-07:002013-08-16T00:56:42.839-07:00It would be nice to conduct the one-way anova for ...It would be nice to conduct the one-way anova for each gender separately, as well.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-61368479462328460762012-01-31T21:42:35.480-07:002012-01-31T21:42:35.480-07:00The ANOVA pairwise comparisons tutorial may be hel...The ANOVA pairwise comparisons tutorial may be helpful to you, since it shows a number of methods for making comparisons. Links to all of the ANOVA tutorials can be found under the ANOVA heading on the right-hand side of this blog. Remember to keep track of your Type I error/significance threshold as you make many comparisons.John M. Quickhttp://www.blogger.com/profile/05331039307550313006noreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-60043308696706307992012-01-31T10:24:33.909-07:002012-01-31T10:24:33.909-07:00This is a really great tutorial. Thanks for puttin...This is a really great tutorial. Thanks for putting it up. Is there anyway to compare all treatments and all genders to each other?Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-62649301306376508942011-10-26T18:17:48.538-07:002011-10-26T18:17:48.538-07:00This sounds like a good question for the R-Help li...This sounds like a good question for the R-Help listserv, assuming it hasn't been addressed already. You're more likely to find someone who is simultaneously an expert in the literature, methods, and code there.John M. Quickhttp://www.blogger.com/profile/05331039307550313006noreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-66959011210351637872011-10-26T17:51:30.094-07:002011-10-26T17:51:30.094-07:00Sorry for late response.
Quinn & Keough (2002)...Sorry for late response.<br />Quinn & Keough (2002) recommend a simple main effects tests that instead use the MS resid from the full, 2way analysis. The logic is this: the best possible estimate of residual variance is given in the full model, using all the data. By splitting the data into 3, and using the MSresid from each of those smaller data sets, we lose power. Are you aware of any procedure in R that let us do such test. I.e. something like this: <br /><br />full.lm = lm(StressReduction ~ Treatment * Gender, dataTwoWayInteraction)<br />medical.lm = MainEffect(full.lm, at=Treatment=="medical")<br />mental.lm = MainEffect(full.lm, at=Treatment=="mental")<br />physical.lm = MainEffect(full.lm, at=Treatment=="physical")<br /><br />So that each of the 3 models testing for the effects of sex, uses the MS res from the "full.lm" model.pandreassvenssonhttp://pandreassvensson.wordpress.com/noreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-24307712526779287682011-03-23T13:10:13.324-07:002011-03-23T13:10:13.324-07:00It is not clear that you are using the model error...It is not clear that you are using the model error for your SME analysis or your pairwise comparisons. With between subject factors in a two factor ANOVA, it is reasonable to use the total model error, and not just the error from the subsets. If the two factors were within subjects, I'd change my tune and do as your did, because assumptions of sphericity are needed.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-70568123821893369452011-02-09T07:37:49.400-07:002011-02-09T07:37:49.400-07:00Thanks for the tip. That is very useful.Thanks for the tip. That is very useful.John M. Quickhttp://www.blogger.com/profile/05331039307550313006noreply@blogger.comtag:blogger.com,1999:blog-6710487119650146215.post-55089630121351687572011-02-09T03:51:02.353-07:002011-02-09T03:51:02.353-07:00I'd strongly recommend adding two way table of...I'd strongly recommend adding two way table of means<br /><br />tapply(StressReduction,list(Gender,Treatment),mean)Giles Whttp://www.blogger.com/profile/14227061673785721046noreply@blogger.com