?s 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 at 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 at 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 at 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