Re: [R] pairwise.var.test
On Mon, Oct 31, 2022 at 5:30 AM Thomas Subia via R-help
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
> 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 CD E F
> > A 1 0.74640.01870.0865 0.0122 0.4693
> > B 0.74641 0.03580.1502 0.0173 0.3240
> > C 0.01870.035810.5131 0.7185 0.0050
> > D 0.08650.15020.51311 0.3240 0.0173
> > E 0.01220.01730.71850.3240 1 0.0029
> > F 0.46930.32400.00500.0173 0.0029 1
> >
> > pvar.mat<-as.matrix(read.table(text=
> > "1 0.74640.01870.0865 0.0122 0.4693
> > 0.74641 0.03580.1502 0.0173 0.3240
> > 0.01870.035810.5131 0.7185 0.0050
> > 0.08650.15020.51311 0.3240 0.0173
> > 0.01220.01730.71850.3240 1 0.0029
> > 0.46930.32400.00500.0173 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
> > 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 BCDE
> >> B 0.7464- - - -
> >> C 0.01870.0358- - -
> >> D 0.08650.15020.5131--
> >> E 0.01220.01730.71850.3240-
> >> F 0.46930.32400.00500.01730.0029
> >>
> >> 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() ->
Re: [R] pairwise.var.test
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
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
> 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$p.value <- c("<0.01", "<0.05", "NS", "NA")[i]
clrs <- setNames(c("green", "blue", "lightgrey", "white"),
c("<0.01", "<0.05", "NS", "NA"))
ggplot(pvar.long, aes(id, name, fill = p.value)) +
geom_tile() +
scale_y_discrete(limits = rev) +
scale_fill_manual(values = clrs) +
theme_bw()
Hope this helps,
Rui Barradas
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
Re: [R] pairwise.var.test
Às 21:47 de 30/10/2022, Jim Lemon escreveu:
Hi Thomas,
I have assumed the format of your p-value matrix. This may require
some adjustment.
A B C D E F
A 1 0.7464 0.01870.0865 0.0122 0.4693
B 0.7464 1 0.03580.1502 0.0173 0.3240
C 0.0187 0.0358 1 0.5131 0.7185 0.0050
D 0.0865 0.1502 0.51311 0.3240 0.0173
E 0.0122 0.0173 0.71850.3240 1 0.0029
F 0.4693 0.3240 0.00500.0173 0.0029 1
pvar.mat<-as.matrix(read.table(text=
"1 0.7464 0.01870.0865 0.0122 0.4693
0.7464 1 0.03580.1502 0.0173 0.3240
0.0187 0.0358 1 0.5131 0.7185 0.0050
0.0865 0.1502 0.51311 0.3240 0.0173
0.0122 0.0173 0.71850.3240 1 0.0029
0.4693 0.3240 0.00500.0173 0.0029 1",
stringsAsFactors=FALSE))
rownames(pvar.mat)<-colnames(pvar.mat)<-LETTERS[1:6]
pvar.col<-matrix(NA,nrow=6,ncol=6)
pvar.col[pvar.mat < 1]<-"red"
pvar.col[pvar.mat < 0.05]<-"orange"
pvar.col[pvar.mat < 0.01]<-"green"
library(plotrix)
par(mar=c(6,4,4,2))
color2D.matplot(pvar.mat,cellcolors=pvar.col,
main="P-values for matrix",axes=FALSE)
axis(1,at=seq(0.5,5.5,by=1),labels=LETTERS[1:6])
axis(2,at=seq(0.5,5.5,by=1),labels=rev(LETTERS[1:6]))
color.legend(0,-1.3,2.5,-0.7,c("NA","NS","<0.05","<0.01"),
rect.col=c(NA,"red","orange","green"))
Jim
On Mon, Oct 31, 2022 at 6:34 AM Thomas Subia via R-help
wrote:
Colleagues,
The RVAideMemoire package has a pairwise variance test which one can use to
identify variance differences between group levels.
Using the example from this package,
pairwise.var.test(InsectSprays$count,InsectSprays$spray), we get this output:
Pairwise comparisons using F tests to compare two variances
data: InsectSprays$count and InsectSprays$spray
A B C DE
B 0.7464 - - - -
C 0.0187 0.0358 - - -
D 0.0865 0.1502 0.5131 - -
E 0.0122 0.0173 0.7185 0.3240-
F 0.4693 0.3240 0.0050 0.0173 0.0029
P value adjustment method: fdr
Is there a way to graph the pairwise variance differences so that users can
easily identify the statistically significant variance differences between
group levels?
I can do this using Minitab but I'd prefer using R for this.
Thomas Subia
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Hello,
With Jim's data creation code, here is a ggplot graph.
First coerce to data.frame, then reshape to long format.
Now bin the p-values with the cutpoints 0.01, 0.05 and 1. This is dne
with ?findInterval.
The colors are assigned in the plot code, based on the binned p.values
above.
library(ggplot2)
pvar.mat |> as.data.frame() -> pvar.df
pvar.df$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.
Re: [R] pairwise.var.test
Hard to know what you want. You may wish to take a look here for ideas: https://r-graph-gallery.com/ Cheers, Bert On Sun, Oct 30, 2022 at 12:34 PM 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 DE > 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. > [[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.
Re: [R] pairwise.var.test
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 0.01870.0865 0.0122 0.4693
B 0.7464 1 0.03580.1502 0.0173 0.3240
C 0.0187 0.0358 1 0.5131 0.7185 0.0050
D 0.0865 0.1502 0.51311 0.3240 0.0173
E 0.0122 0.0173 0.71850.3240 1 0.0029
F 0.4693 0.3240 0.00500.0173 0.0029 1
pvar.mat<-as.matrix(read.table(text=
"1 0.7464 0.01870.0865 0.0122 0.4693
0.7464 1 0.03580.1502 0.0173 0.3240
0.0187 0.0358 1 0.5131 0.7185 0.0050
0.0865 0.1502 0.51311 0.3240 0.0173
0.0122 0.0173 0.71850.3240 1 0.0029
0.4693 0.3240 0.00500.0173 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
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 DE
> 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.
