On Mon, Oct 31, 2022 at 5:30 AM Thomas Subia via R-help <r-help@r-project.org> wrote: > > Colleagues, > > Thank you all for the timely suggestions. That is appreciated. > > What I am really looking for a way to identify difference in group level > variance by using multiple comparison intervals. Minitab displays those > results in a graph. > > This method is described in: > https://support.minitab.com/en-us/minitab/20/media/pdfs/translate/Multiple_Comparisons_Method_Test_for_Equal_Variances.pdf > > I was hoping that R had something similar.
Perhaps you are looking for something like the plot produced by example(TukeyHSD) For this you would need confidence intervals for each pairwise comparison, not just the p-values. Once you have those, recreating the plot should not be difficult, but I don't know if there is any package that already does this for you. E.g., car::leveneTest() etc. are designed for multiple groups and won't give you confidence intervals. Best, -Deepayan > > I tried a Google search on this but to no avail. > > Thomas Subia > > > > > > > On Sunday, October 30, 2022 at 03:44:54 PM PDT, Rui Barradas > <ruipbarra...@sapo.pt> wrote: > > > > > > Às 21:47 de 30/10/2022, Jim Lemon escreveu: > > Hi Thomas, > > I have assumed the format of your p-value matrix. This may require > > some adjustment. > > > > A B C D E F > > A 1 0.7464 0.0187 0.0865 0.0122 0.4693 > > B 0.7464 1 0.0358 0.1502 0.0173 0.3240 > > C 0.0187 0.0358 1 0.5131 0.7185 0.0050 > > D 0.0865 0.1502 0.5131 1 0.3240 0.0173 > > E 0.0122 0.0173 0.7185 0.3240 1 0.0029 > > F 0.4693 0.3240 0.0050 0.0173 0.0029 1 > > > > pvar.mat<-as.matrix(read.table(text= > > "1 0.7464 0.0187 0.0865 0.0122 0.4693 > > 0.7464 1 0.0358 0.1502 0.0173 0.3240 > > 0.0187 0.0358 1 0.5131 0.7185 0.0050 > > 0.0865 0.1502 0.5131 1 0.3240 0.0173 > > 0.0122 0.0173 0.7185 0.3240 1 0.0029 > > 0.4693 0.3240 0.0050 0.0173 0.0029 1", > > stringsAsFactors=FALSE)) > > rownames(pvar.mat)<-colnames(pvar.mat)<-LETTERS[1:6] > > pvar.col<-matrix(NA,nrow=6,ncol=6) > > pvar.col[pvar.mat < 1]<-"red" > > pvar.col[pvar.mat < 0.05]<-"orange" > > pvar.col[pvar.mat < 0.01]<-"green" > > library(plotrix) > > par(mar=c(6,4,4,2)) > > color2D.matplot(pvar.mat,cellcolors=pvar.col, > > main="P-values for matrix",axes=FALSE) > > axis(1,at=seq(0.5,5.5,by=1),labels=LETTERS[1:6]) > > axis(2,at=seq(0.5,5.5,by=1),labels=rev(LETTERS[1:6])) > > color.legend(0,-1.3,2.5,-0.7,c("NA","NS","<0.05","<0.01"), > > rect.col=c(NA,"red","orange","green")) > > > > Jim > > > > On Mon, Oct 31, 2022 at 6:34 AM Thomas Subia via R-help > > <r-help@r-project.org> wrote: > >> > >> Colleagues, > >> > >> The RVAideMemoire package has a pairwise variance test which one can use > >> to identify variance differences between group levels. > >> > >> Using the example from this package, > >> pairwise.var.test(InsectSprays$count,InsectSprays$spray), we get this > >> output: > >> > >> Pairwise comparisons using F tests to compare two variances > >> > >> data: InsectSprays$count and InsectSprays$spray > >> > >> A B C D E > >> B 0.7464 - - - - > >> C 0.0187 0.0358 - - - > >> D 0.0865 0.1502 0.5131 - - > >> E 0.0122 0.0173 0.7185 0.3240 - > >> F 0.4693 0.3240 0.0050 0.0173 0.0029 > >> > >> P value adjustment method: fdr > >> > >> Is there a way to graph the pairwise variance differences so that users > >> can easily identify the statistically significant variance differences > >> between group levels? > >> > >> I can do this using Minitab but I'd prefer using R for this. > >> > >> Thomas Subia > >> > >> ______________________________________________ > >> 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. > > > Hello, > > With Jim's data creation code, here is a ggplot graph. > > First coerce to data.frame, then reshape to long format. > Now bin the p-values with the cutpoints 0.01, 0.05 and 1. This is dne > with ?findInterval. > > The colors are assigned in the plot code, based on the binned p.values > above. > > > library(ggplot2) > > pvar.mat |> as.data.frame() -> pvar.df > pvar.df$id <- row.names(pvar.df) > > pvar.df |> tidyr::pivot_longer(-id, values_to = "p.value") -> pvar.long > i <- findInterval(pvar.long$p.value, c(0, 0.01, 0.05, 1)) > pvar.long$p.value <- c("<0.01", "<0.05", "NS", "NA")[i] > clrs <- setNames(c("green", "blue", "lightgrey", "white"), > c("<0.01", "<0.05", "NS", "NA")) > > ggplot(pvar.long, aes(id, name, fill = p.value)) + > geom_tile() + > scale_y_discrete(limits = rev) + > scale_fill_manual(values = clrs) + > theme_bw() > > > Hope this helps, > > Rui Barradas > > ______________________________________________ > 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.