The nature of her inquiries suggests to me that Carol strongly needs to consult a local statistician rather than fooling around with this list.
-- Bert On Fri, Jul 20, 2012 at 11:56 AM, John Fox <j...@mcmaster.ca> wrote: > Dear Carol, > >> -----Original Message----- >> From: carol white [mailto:wht_...@yahoo.com] >> Sent: July-20-12 2:45 PM >> To: John Fox >> Subject: Re: inverse normal transformation >> >> Thanks John for your quick reply. >> >> The purpose of applying inverse normal transformation is to reduce the >> impact of outliers and deviations from normality on statistical >> analysis. > > In other words, you're forcing the variable to follow a normal distribution > and making the units of measurement uninterpretable. I'll assume that this > somehow makes sense. > >> >> Indeed, it includes the steps that you went through. However, I don't >> know why you calculated (rank - 0.5)/20 to get the p-value. Then, how >> could we convert the quantiles (Q) into normal deviates? > > They *are* quantiles on the standard normal scale -- that's what qnorm() > provides (with the default mean of 0 and standard deviation of 1). The > cumulative probabilities (not p-values) are calculated from the order > statistics of your data, where subtracting 0.5 avoids cumulative > probabilities of 0 or 1. This (or something close to it) is standard for > computing comparison quantiles. > > I'm copying this message to r-help (with the original subject line) since > the discussion there continues. > > Best, > John > >> >> Many thanks, >> >> Carol >> >> >> ________________________________ >> >> From: John Fox <j...@mcmaster.ca> >> To: 'carol white' <wht_...@yahoo.com> >> Sent: Friday, July 20, 2012 4:43 PM >> Subject: RE: inverse normal transformation >> >> >> Dear Carol, >> >> Like the people on r-help list who tried to help you, I have no idea >> why you >> want to do this. If you're trying to get the corresponding standard >> normal >> quantiles for your data, as for a QQ plot (and why else you might want >> them >> isn't clear to me), you can simply compute >> >> rank <- rank(tmp) >> P <- (rank - 0.5)/20 >> Q <- qnorm(P) >> >> Then, the QQ plot is >> >> plot(Q, tmp) >> >> Best, >> John >> >> -------------------------------- >> John Fox >> Senator William McMaster >> Professor of Social Statistics >> Department of Sociology >> McMaster University >> Hamilton, Ontario, Canada >> http://socserv.mcmaster.ca/jfox >> >> >> >> >> > -----Original Message----- >> > From: carol white [mailto:wht_...@yahoo.com] >> > Sent: July-20-12 9:08 AM >> > To: j...@mcmaster.ca >> > Subject: inverse normal transformation >> > >> > Dear John, >> > >> > >> > Are the following scripts correct to get the inverse normal >> > transformation of a data set? >> > >> > >> > Thanks for your help, >> > >> > >> > Carol >> > ----------------------------------------------- >> > >> > tmp >> > [1] 2.502519 1.828576 3.755778 17.415000 3.779296 2.956850 >> > 2.379663 [8] 1.103559 8.920316 2.744500 2.938480 7.522174 >> > 10.629200 8.552259 [15] 5.425938 4.388906 0.000000 0.723887 >> > 11.337860 3.763786 >> > >> > >> > tmp.p =2*pnorm(abs(scale(tmp)),lower.tail=FALSE) >> > > tmp.qnorm = qnorm(tmp.p/2,lower.tail=FALSE) tmp.qnorm = >> > > qnorm(tmp.p/2,lower.tail=FALSE) >> > >> > > par(mfrow = c(1,3)) >> > > hist(tmp) >> > > hist(tmp.p) >> > > hist(tmp.qnorm) >> > >> >> >> >> > > ______________________________________________ > R-help@r-project.org mailing list > 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. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ R-help@r-project.org mailing list 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.