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. 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.
