Inspired by the responses, I tried to do this analytically.
The idea is that truncated mean and standard deviation could be expressed as
integral forms. So if given truncated mean, sd and truncated point (mut, sdt,
thre), an optim( ) function could be writen to get the parameters. But the
problem is, pdf is needed in advance to shape the normal curve. So I think it
is possible to do this in an iterative optimization, given assummed initial
sigma and mu, if the optimization meets requirements, then the sigma and mu
could be considered as the real numbers.
I tried to do these by :
f <- function(x,sigma,mu) (1/(sigma*sqrt(2*pi)))*exp(-(x-mu)^2/(2*sigma^2))
pdf.fun <- function(x) x*f(x);
sd.fun <- function(x) x^2*f(x); #---------- define a few functions
solve.fun <- function(sigma,mu,thre,mut,sdt)
{
(mut-integrate(pdf.fun,thre,upper=Inf)$value/integrate(f,thre,upper=Inf)$value)^2
+(sdt -
integrate(sd.fun,thre,upper=Inf)$value/integrate(f,thre,upper=Inf)$value-(integrate(pdf.fun,thre,upper=Inf)$value/integrate(f,thre,upper=Inf)$value)^2)^2
}
I wish this solve.fun ( ) could be minimized and then gives minimum <= 5
for( i in 1:100)
{
mu <- 200;sigma <- 20;
thre <- 160;
mut <- 230; sdt <- 15;
sol.tem <- optimize(solve.fun, lower =0.1,upper =100,tol=0.001);
if (sol.tem$minimum<= 5) return(sol.tem)
}
I know my codes is just awkward, and not really working. But I expect some
advice and suggestion about the methods. Am I going in a wrong way since I have
been working on it for a long time. Thanks a lot!
Jen
-----Original Message-----
From:Ritwik Sinha , [EMAIL PROTECTED]
Sent: 2006-09-12, 17:20:04
To:
CC:jennystadt; [email protected]
Subject: Re: [R] About truncated distribution
However, if you know the point(s) of truncation then you should be able to work
your way back. Look for the mean and variance of a truncated normal, it will
involve mu, sigma and c (point of truncation). You will need to solve for mu
and sigma from two equation. For example look at the wikipedia page on normal
distribution, it has the mean of a truncated normal distribution. Many standard
statistics books should have the rest of the information.
On 9/12/06, Berton Gunter <[EMAIL PROTECTED]> wrote:
>
> But my question is a bit different. What I know is the mean
> and sd after truncation. If I assume the distribution is
> normal, how I am gonna develope the original distribution
> using this two parameters?
You can't, as they are plainly not sufficient (you need to know the amount
of truncation also). If you have only the mean and sd and neither the actual
data nor the truncation point you're through.
-- Bert Gunter
Genentech
Could anybody give me some advice?
> Thanks in advance!
>
> Jen
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
______________________________________________
[email protected] 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.
--
Ritwik Sinha
Graduate Student
Epidemiology and Biostatistics
Case Western Reserve University
http://darwin.cwru.edu/~rsinha
[[alternative HTML version deleted]]
______________________________________________
[email protected] 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.
