[R] Bca confidence intervals for Pearson coefficient, thanks.

[John Fox]

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:varinsacha at 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 <jfox at mcmaster.ca>
> À : varin sacha <varinsacha at yahoo.fr>
> Cc : r-help help <r-help at 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 <varinsacha at 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]]
> >
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> 
> 
> 
> 
>