Dear Dennis, I really appreciated for your and Bert's help. I read the paper and it seems that once the study is completed, power calculations do not inform us in any way as to the conclusions of the present study. But I am really now confused whether we can't improve the research design for future or next year monitoring based on the present results. I would really be grateful for your suggestions and insights. Can't we take reference from the present study for improving future sampling?Thanks KG
> Date: Sat, 8 Nov 2014 13:36:35 -0800 > Subject: Re: [R] how to determine power in my analysis? > From: djmu...@gmail.com > To: kristi.glo...@hotmail.com > CC: gunter.ber...@gene.com; r-h...@stat.math.ethz.ch > > Hi Kristi: > > I think this paper elucidates the problem Bert mentioned. A thorough > and careful reading of the last two sections should clarify what > post-hoc power is and is not. > > http://www.stat.uiowa.edu/files/stat/techrep/tr378.pdf > > Dennis > > On Sat, Nov 8, 2014 at 11:25 AM, Kristi Glover > <kristi.glo...@hotmail.com> wrote: > > Hi Bert, Thanks for the message. So far I know we can test whether my > > sample size in my analysis is enough or not. It is also post hoc property. > > For example, we can calculate standard deviations, error variance etc in > > the data sets, and then we can use them to determine whether the sample > > size was enough or not with certain level of alpha and power. we can do it > > is some of the statistical programs, but I was not aware in R. thanks KG > > > >> Date: Sat, 8 Nov 2014 10:55:56 -0800 > >> Subject: Re: [R] how to determine power in my analysis? > >> From: gunter.ber...@gene.com > >> To: kristi.glo...@hotmail.com > >> CC: r-h...@stat.math.ethz.ch > >> > >> Kristi: > >> > >> Power is a prespecified property of the design, not a post hoc > >> property of the analysis (SAS procedures notwithstanding). So you're a > >> day late and a dollar short. > >> > >> I suggest you consult with a local statistician about such matters, as > >> you appear to be out of your depth. > >> > >> Cheers, > >> Bert > >> > >> Bert Gunter > >> Genentech Nonclinical Biostatistics > >> (650) 467-7374 > >> > >> "Data is not information. Information is not knowledge. And knowledge > >> is certainly not wisdom." > >> Clifford Stoll > >> > >> > >> > >> > >> On Sat, Nov 8, 2014 at 3:49 AM, Kristi Glover <kristi.glo...@hotmail.com> > >> wrote: > >> > Hi R Users, > >> > I was trying to determine whether I have enough samples and power in my > >> > analysis. Would you mind to provide some hints?. I found a several > >> > packages for power analysis but did not find any example data. I have > >> > two sites and each site has 4 groups. I wanted to test whether there was > >> > an effect of restoration activities and sites on the observed value. I > >> > used a two way factorial ANOVA and now I wanted to test the power of the > >> > analysis (whether the sample sizes are enough for the analysis? what are > >> > the alpha and power in the analysis using this data set? if it is not > >> > enough, how much samples should be collected for alpha 0.05 and > >> > power=0.8 and 0.9 for the analysis (two way factorial analysis). > >> > The example data:data<-structure(list(observedValue = c(0.08, 0.53, > >> > 0.14, 0.66, 0.37, 0.88, 0.84, 0.46, 0.3, 0.61, 0.75, 0.82, 0.67, 0.37, > >> > 0.95, 0.73, 0.74, 0.69, 0.06, 0.97, 0.97, 0.07, 0.75, 0.68, 0.53, 0.72, > >> > 0.34, 0.12, 0.49, 0.77, 0.45, 0.07, 0.97, 0.34, 0.68, 0.48, 0.65, 0.7, > >> > 0.57, 0.66, 0.4, 0.29, 0.88, 0.36, 0.68, 0.32, 0.8, 0, 0.11, 0.48, 0.85, > >> > 0.94, 0.12, 0.12, 0, 0.89, 0.66, 0.2, 0.57, 0.09, 0.27, 0.81, 0.53, > >> > 0.09, 0.5, 0.41, 0.89, 0.47, 0.39, 0.85, 0.71, 0.89, 0.01, 0.71, 0.42, > >> > 0.72, 0.62, 0.3, 0.56, 0.99, 0.97, 0.03, 0.09, 0.27, 0.27, 0.94, 0.23, > >> > 0.97, 0.81, 0.95), condition = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, > >> > 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > >> > 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, > >> > 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > >> > 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, > >> > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("g! ood! > > ", "! > >> > medium", "poor", "verygood"), class = "factor"), areas = > >> > structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > >> > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > >> > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, > >> > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > >> > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > >> > 2L, 2L, 2L), .Label = c("Restored", "unrestored"), class = "factor")), > >> > .Names = c("observedValue", "condition", "areas"), class = "data.frame", > >> > row.names = c(NA, -90L)) > >> > test= aov(observedValue~condition*areas,data=data)summary(test) > >> > power of the analysis? > >> > thanks for your help. > >> > Sincerely, KG > >> > > >> > [[alternative HTML version deleted]] > >> > > >> > ______________________________________________ > >> > 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. > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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. [[alternative HTML version deleted]] ______________________________________________ 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.
