Thank you Jim, I did see those (though not my typo :) and am still pondering the warning about post-hoc analyses.
The situation that I am in is that I have a set of individuals who have been assigned a course grade. We have then clustered these individuals into about 50 communities using standard community detection algorithms with the goal of determining whether community membership affects one of their grades. We are using the KW test as the grade data is strongly non-normal and my coauthors preferred KW as an alternative. The two issues that I am struggling with are: 1) whether the post-hoc power analysis would be useful; and 2) how to code the simulation studies that are described in: http://onlinelibrary.wiley.com/doi/10.1002/bimj.4710380510/abstract Problem #1 is of course beyond the scope of this e-mail list though I would welcome anyone's suggestions on that point. I am not sure that I buy the arguments against it offered here: http://graphpad.com/support/faq/why-it-is-not-helpful-to-compute-the-power-of-an-experiment-to-detect-the-difference-actually-observed-why-is-post-hoc-power-analysis-futile/ It seems that the rationale boils down to "you didn't find it so you couldn't find it" but that does not tell me how far off I was from the goal. I am still perusing the articles the author cites however. With respect to question #2 I am trying to lay my hands on the article and did find this old r-help discussion: http://r.789695.n4.nabble.com/Power-of-Kruskal-Wallis-Test-td4671188.html however I am not sure how to adapt the simulation studies that it links to to my current problem. The links it leads to focus on mixed-effects models. This may be more of a pure stats question and not suited for this list but I thought I'd ask in the hopes that anyone had any more specific KW code or knew of a good tutorial for the right kinds of simulation studies. Thank you, Collin. On Thu, Apr 2, 2015 at 6:35 PM, Jim Lemon <drjimle...@gmail.com> wrote: > Hi Collin, > Have a look at this: > > http://stats.stackexchange.com/questions/70643/power-analysis-for-kruskal-wallis-or-mann-whitney-u-test-using-r > > Although, thinking about it, this might have constituted your "perusal of > the literature". > > Plus it always looks better when you spell the names properly > > Jim > > > On Fri, Apr 3, 2015 at 2:23 AM, Jeff Newmiller <jdnew...@dcn.davis.ca.us> > wrote: >> >> Please stop... you are acting like a broken record, and are also posting >> in HTML format. Please read the Posting Guide and demonstrate that you have >> used a search engine on this topic before posting again. >> >> --------------------------------------------------------------------------- >> Jeff Newmiller The ..... ..... Go >> Live... >> DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live >> Go... >> Live: OO#.. Dead: OO#.. Playing >> Research Engineer (Solar/Batteries O.O#. #.O#. with >> /Software/Embedded Controllers) .OO#. .OO#. >> rocks...1k >> >> --------------------------------------------------------------------------- >> Sent from my phone. Please excuse my brevity. >> >> On April 2, 2015 7:25:20 AM PDT, Collin Lynch <cfly...@ncsu.edu> wrote: >> >Greetings, I am working on a project where we are applying the >> >Kruskal-Wallace test to some factor data to evaluate their correlation >> >with >> >existing grade data. I know that the grade data is nonnormal therefore >> >we >> >cannot rely on ANOVA or a similar parametric test. What I would like >> >to >> >find is a mechanism for making power calculations for the KW test given >> >the >> >nonparametric assumptions. My perusal of the literature has suggested >> >that >> >a simulation would be the best method. >> > >> >Can anyone point me to good examples of such simulations for KW in R? >> >And >> >does anyone have a favourite package for generating simulated data or >> >conducting such tests? >> > >> > Thank you, >> > Collin. >> > >> > [[alternative HTML version deleted]] >> > >> >______________________________________________ >> >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> >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 -- To UNSUBSCRIBE and more, see >> 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 -- To UNSUBSCRIBE and more, see 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.
