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) - 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@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.
