[R] Beta distribution approximate to Normal distribution

[David L Carlson]

There are also truncated normal distributions in packages truncnorm, crch, and msm. Also a multivariate truncated normal distribution in package tmvtnorm.

e.g.

library(truncnorm)
x <- rtruncnorm(n, a=0, mean=u, sd=a)
hist(x)

library(msm)
x <- rtnorm(n, mean=u, sd=a, lower=0)
hist(x)

-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352

-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Rui Barradas
Sent: Tuesday, September 15, 2015 12:41 PM
To: Ted.Harding at wlandres.net; r-help at r-project.org
Cc: Chien-Pang Chin
Subject: Re: [R] Beta distribution approximate to Normal distribution

Hello,

If you want a truncated something rng, you can use the following 
function. Note that 'distr' is the name of an R distribution function 
without the dpqr prefix.

rtrunc <- function(n, distr, lower = -Inf, upper = Inf, ...){
	makefun <- function(prefix, FUN, ...){
		txt <- paste(prefix, FUN, "(x, ...)", sep = "")
		function(x, ...) eval(parse(text = txt))
	}
	if(length(n) > 1) n <- length(n)
	pfun <- makefun("p", distr, ...)
	qfun <- makefun("q", distr, ...)
	lo <- pfun(lower, ...)
	up <- pfun(upper, ...)
	u <- runif(n, lo, up)
	qfun(u, ...)
}

u <- 2; a <- 3
x <- rtrunc(1000, "norm", lower = 0, mean = u, sd = a)
hist(x)


Hope this helps,

Rui Barradas

Em 15-09-2015 16:12, (Ted Harding) escreveu:
> Using non-central chi-squared (especially with df=1) is unlikely
> to generate random numbers anywhere near a Normal distribution
> (see below).
>
> And "rchisq(100, df=1, ncp=u/a)" won't work anyway with u<0,
> since ncp must be >= 0 (if < 0 then all are NA).
>
> Better to shoot straight for the target (truncated Normal), though
> several shots are likely to be required! For example (code which
> spells it out), taking u=3 and a=2:
>
>    n <- 100
>    u <- 3 ; a <- 2
>    x <- NULL
>    N <- length(x)
>    while(N < n){
>      x <- c(x,rnorm(n,mean=u,sd=a))
>      x <- x[x>0]
>      N <- length(x)
>    }
>    x <- x[1:n]
>
> Comparison with non-central chi-squared:
>
>    y <- rchisq(100, df=1, ncp=u/a)
>    hist(x)
>    hist(y)
>
>
>
> On 15-Sep-2015 13:26:44 JLucke at ria.buffalo.edu wrote:
>> Your question makes no sense as stated.  However, guessing at what you
>> want, you should  perhaps consider the non-central chi-square density with
>> 1 df and ncp = u/a, i.e,
>>
>> rchisq(100, df=1, ncp=u/a)
>>
>> Joe
>> Joseph F. Lucke, PhD
>> Senior Statistician
>> Research Institute on Addictions
>> University at Buffalo
>> State University of New York
>> 1021 Main Street
>> Buffalo, NY  14203-1016
>>
>> Chien-Pang Chin <chienpang.c at gmail.com>
>> Sent by: "R-help" <r-help-bounces at r-project.org>
>> 09/15/2015 06:58 AM
>>
>> To
>> "r-help at r-project.org" <r-help at r-project.org>,
>>
>> Subject
>> [R] Beta distribution approximate to Normal distribution
>>
>> Hi,
>> I need to generate 1000 numbers from N(u, a^2), however I don't
>> want to include 0 and negative values. How can I use beta distribution
>> approximate to N(u, a^2) in R.
>>
>> Thx for help
>
> -------------------------------------------------
> E-Mail: (Ted Harding) <Ted.Harding at wlandres.net>
> Date: 15-Sep-2015  Time: 16:12:35
> This message was sent by XFMail
>
> ______________________________________________
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>

______________________________________________
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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.