[R] function for inverse normal transformation
wht_crl at yahoo.com
Fri Jul 20 13:59:14 CEST 2012
I have a continuous data. So to calculate the inverse normal transformation, I thought that I should first calculate the Z-score normalized data and then, calculate the p-value et the quantile transformation. Does this seem to be more sensible
The attached file shows the histogram of a small data set before transformation, the p-value generated from the Z-score normalized data and then, the qnorm-transformed data.
Thanks for your feedback,
From: Duncan Murdoch <murdoch.duncan at gmail.com>
To: carol white <wht_crl at yahoo.com>
Cc: "r-help at stat.math.ethz.ch" <r-help at stat.math.ethz.ch>
Sent: Friday, July 20, 2012 1:42 PM
Subject: Re: [R] function for inverse normal transformation
On 12-07-20 7:36 AM, carol white wrote:
> Thanks for your reply.
> So to derive it from a given data set, is the following correct to do?
> my_data.p =2*pnorm(abs(my_data),lower.tail=FALSE)
> my_data.q = qnorm(my_data.p)
I don't know what you're trying to do, but that doesn't look like it
does something sensible. It would take a value like 2, compute the p to
be 0.045, and return the corresponding quantile of the normal
distribution, i.e. -1.69 or so. I don't know why you'd want to do that.
> From: Duncan Murdoch <murdoch.duncan at gmail.com>
> To: carol white <wht_crl at yahoo.com>
> Cc: "r-help at stat.math.ethz.ch" <r-help at stat.math.ethz.ch>
> Sent: Friday, July 20, 2012 1:23 PM
> Subject: Re: [R] function for inverse normal transformation
> On 12-07-20 6:21 AM, carol white wrote:
>> What is the function for inverse normal transformation?
> Duncan Murdoch
>> [[alternative HTML version deleted]]
>> R-help at r-project.org mailing list
>> 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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