Dear Professor Fox,
Once more I really thank you lots for your response. I will try it.
Best regards, and Merry Christmas to you.
SV
De : John Fox <j...@mcmaster.ca>
À : 'varin sacha' <varinsa...@yahoo.fr>
Cc : 'r-help help' <r-help@r-project.org>
Envoyé le : Dimanche 21 décembre 2014 22h22
Objet : RE: [R] Bca confidence intervals for Pearson coefficient, thanks.
Dear Sacha,
Simply write a function that takes a data set and index vector as arguments,
say statistic(data, index), and have it compute and return either eta^2 or V
depending upon the application. Use the function myCor() in the previous
example as a model.
Best,
John
> -----Original Message-----
> From: varin sacha [mailto:varinsa...@yahoo.fr]
> Sent: Sunday, December 21, 2014 3:03 PM
> To: John Fox
> Cc: r-help help
> Subject: Re: [R] Bca confidence intervals for Pearson coefficient,
> thanks.
>
> Dear Professor FOX,
>
>
> I really thank you lots for all your precisions.
> One last precision, now if I want tot calculate the BCa bootstrap CIs
> for the Cramer's V and the Eta-squared.
>
>
> ## Read in the data file, using headers and Tab separator
> > test = read.table(file.choose(), header = TRUE, sep = "\t")
>
>
> ## Check the variable names
> > names(test)
> [1] "gender" "option" "math.test" "geo.test" "shopping" "sports"
>
>
> ## Cramer's V
>
> > library(questionr)
> > tab = table(test$gender, test$option)
> > cramer.v(tab)
> [1] 0.1490712
>
> ## Eta.square
>
> > library(lsr)
> > test.aov = aov(math.test ~ gender, data = test)
>
> > etaSquared(test.aov)
> eta.sq eta.sq.part
> gender 0.1207154 0.1207154
>
>
>
> Best Regards, looking forward to reading you once more,
>
> Sacha
>
> ________________________________
>
> De : John Fox <j...@mcmaster.ca>
> À : varin sacha <varinsa...@yahoo.fr>
> Cc : r-help help <r-help@r-project.org>
> Envoyé le : Dimanche 21 décembre 2014 5h33
> Objet : Re: [R] Bca confidence intervals for Pearson coefficient,
> thanks.
>
>
> Dear varin sacha,
>
> I think that you misunderstand how boot() and boot.ci() work. The boot()
> function in the simplest case takes two arguments, for the data and
> indices into the data, while boot.ci() takes as its principal argument
> the object returned by boot(). All of this seems reasonably clear in
> ?boot and ?boot.ci.
>
> Here's an example with different data (since as far as I can see you
> didn't supply yours):
>
> ------------- snip --------
>
> > library(boot)
> >
> > x <- longley$Year
> > y <- longley$Population
> >
> > cor(cbind(x, y))
> x y
> x 1.0000000 0.9939528
> y 0.9939528 1.0000000
> >
> > myCor <- function(data, index){
> + cor(data[index, ])[1, 2]
> + }
> >
> > set.seed(12345)
> > (b <- boot(data=cbind(x, y), statistic=myCor, R=200))
>
> ORDINARY NONPARAMETRIC BOOTSTRAP
>
>
> Call:
> boot(data = cbind(x, y), statistic = myCor, R = 200)
>
>
> Bootstrap Statistics :
> original bias std. error
> t1* 0.9939528 0.0008263766 0.001850004
>
> > boot.ci(b, type="bca")
> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
> Based on 200 bootstrap replicates
>
> CALL :
> boot.ci(boot.out = b, type = "bca")
>
> Intervals :
> Level BCa
> 95% ( 0.9895, 0.9969 )
> Calculations and Intervals on Original Scale
> Warning : BCa Intervals used Extreme Quantiles
> Some BCa intervals may be unstable
> Warning message:
> In norm.inter(t, adj.alpha) : extreme order statistics used as endpoints
>
> --------------- snip ---------------
>
> Note that 200 bootstrap replications are generally sufficient for
> bootstrap standard errors (a normal-theory CI would be a poor choice
> here, unless you transform the correlation coefficient), but really
> aren't enough for a BCa interval.
>
> I hope this helps,
> John
>
>
>
>
> ------------------------------------------------
> John Fox, Professor
> McMaster University
> Hamilton, Ontario, Canada
> http://socserv.mcmaster.ca/jfox/
>
> On Sat, 20 Dec 2014 20:36:57 +0000 (UTC)
> varin sacha <varinsa...@yahoo.fr> wrote:
> > Hi to everyone,
> > I am trying to calculate the Bca bootstrap confidence intervals for
> the Pearson coefficient.
> > x=Dataset$math.testy=Dataset$geo.testcor(x,y,method="pearson")[1]
> 0.6983799
> > boot.ci(cor, conf=0.95, type=bca)Erreur dans boot.out$t0 : objet de
> type 'closure' non indiçable
> >
> > I have tried as well to calculate the Pearson coefficient using
> bootstrap and then to calculate the Bca bootstrap CIs of the Pearson. It
> doesn't work either.
> > boot(data = cbind(x, y), statistic = cor, R = 200)
> >
> > ORDINARY NONPARAMETRIC BOOTSTRAP
> >
> >
> > Call:
> > boot(data = cbind(x, y), statistic = cor, R = 200)
> >
> >
> > Bootstrap Statistics :
> > original bias std. error
> > t1* -0.6243713 0.6295142 0.2506267
> > t2* -0.1366533 0.1565392 0.2579134
> > > boot.ci(cor, conf=0.95, type=bca)
> > Erreur dans boot.out$t0 : objet de type 'closure' non indiçable
> > Many thanks to tell me how to correct my R script to get the Bca CIs
> for my Pearson coefficient. Best regards, looking forward to reading
> you,
> > SV
>
> >
> > [[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.
>
>
>
>
>
>
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