[R] newbie (me) needs to model distribution as two overlappi

[(Ted Harding)]

On 19-Feb-08 18:09:18, Monica Pisica wrote:
> take a look at:
> 
> Du, 2002, Master Thesis, http://www.math.mcmaster.ca/peter/mix/Rmix.pdf
> 
> Macdonald, P., 2003, RMIX routine for R,
> http://www.math.mcmaster.ca/peter/mix/mix.html
> 
> I don't think this package was actually posted on CRAN (the mix package
> on CRAN is a different one as far as i remember)

The CRAN 'mix' package is indeed different -- it deals with
multiple imputation of missing values in multivariate data
of "mixed types", i.e. some variables are continuous, some are
categorical. It has nothing whatever to do with mixtures of
distributions (except insofar as it may be possible, in special
cases, to model a mixture of distibutions within the imputation
framework embodied in 'mix').

It seems that MacDonald's Rmix package is now calle 'mixdist'
(see the above "mix.html" URL), and that name is not listed
on CRAN. However (again see the above URL) versions of it can
be downloaded from MacDonald's website.

Ted.

 - maybe because
> Macdonald has also a commercial package - or maybe had .... but you can
> model your distribution with 2 or maybe more normal or log-normal
> distributions - as you see fit - and does an ANOVA test to see if your
> modeling is statistically significant or not. You will get also mean
> and standard distribution for each of your modeling distributions.
> 
> Hope this helps,
> 
> Monica
> 
> ______________________________________________________________
> Message: 41
> Date: Mon, 18 Feb 2008 16:03:10 +0000
> From: 
> Subject: [R] newbie (me) needs to model distribution as two
> overlapping gaussians
> To: 
> Message-ID: 
> Content-Type: text/plain; charset="iso-8859-1"
> 
> 
> Recently, I have been working with some data that look like two
> overlapping gaussian distributions. I would like to either
> 
> 1) determine the mean and SD for each of the two distributions
> 
> OR
> 
> 2) get some (bayesian ?) statistic that estimates how likely an
> observation is to belong to the left-hand or right-hand distribution
> 
> In case I'm using the wrong language, my data looks something like
> this:
> 
> B <- rnorm(500,40,10)
> H <- rnorm(500,80,5 )
> N <- runif(200,0,99)
> D <- c(B,H,N)
> 
> Where B=background, H=hits, N=noise, and D=my observed distribution
> 
> I have seen analyses like this in the past, but I can't remember what
> it is called. If somebody out there can point me towards an R function,
> or even the cannonical name for this kind of model, I think I can write
> the necessary code.
> 
> Thanks in advance,
> Mark
> 
> 
> 
> _________________________________________________________________
> [[elided Hotmail spam]]
> 
> ______________________________________________
> R-help at 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.

--------------------------------------------------------------------
E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
Fax-to-email: +44 (0)870 094 0861
Date: 19-Feb-08                                       Time: 18:46:08
------------------------------ XFMail ------------------------------