I am looking for procedure that allow one to fit multiple distributions to a variable. For example, based on analysis of the data we suppose that the data can be represented by 3-5 normal distributions added together. I would like to be able to determine the mean, sd, and weight associated with each distribution and examine the improvement of the fit when 3,4 or 5 normal distributions are used as components of the observed data.
The goal is to be able to separate out the background observations from impacted observations and be able to develop summary stats that describe background/baseline. I have been experimenting with mclust package but I am not convinced this is the best/simplest way to proceed. I am hoping to add this sort of analysis to some of the other ways were are able to characterized background and compare them all. This is for the evaluation of chemicals in the environment. Michael J. Bock, PhD | Manager ENVIRON | www.environcorp.com <http://www.environcorp.com/> 136 Commercial Street, Suite 402 Portland, ME 04101 V: 207.347.4413 x223| F: 207.347.4384 |[EMAIL PROTECTED] This message contains information that may be confidenti...{{dropped:11}} ______________________________________________ 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.
