[R] newbie (me) needs to model distribution as two overlapping gaussians
Greg.Snow at imail.org
Tue Feb 19 18:31:50 CET 2008
The mixreg package may help. Just fit an intercept only model to your
data (may need to have x be a column of 1's and tell it not to include
an intercept) and see if it finds your mixtures.
Hope this helps,
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
greg.snow at imail.org
> -----Original Message-----
> From: r-help-bounces at r-project.org
> [mailto:r-help-bounces at r-project.org] On Behalf Of tcornulier
> Sent: Monday, February 18, 2008 12:48 PM
> To: mmiller at mail.usp.edu
> Cc: r-help at r-project.org
> Subject: Re: [R] newbie (me) needs to model distribution as
> two overlapping gaussians
> Is a finite mixture of 2 gaussians the name you are looking for?
> This specific model will not deal with your N component however.
> you can find some functions here:
> mmiller at mail.usp.edu wrote:
> > 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
> > ______________________________________________
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> Thomas Cornulier
> Mammal Research Institute
> Polish Academy of Sciences
> ul. Waszkiewicza 1c
> 17-230 Bialowieza
> tel (0048) 85 682 77 88
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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