Joke, Two other places to help you with your objectives in fitting univariate normal mixtures are: 1) The mclust package by Raftery and Fraley (available at CRAN). Their EMclust() function, for example, lets you specify a range of "number of components" to fit multiple models as well as the ability to specify whether to assume equal variances or not. The Schwarz (BIC / SBC) criterion is used to help distinguish goodness-of-fit amongst the models fitted. I have found the fitting routines to be more-than-quick enough under Linux, but did run into problems when running the same code under Windows. 2) The Venables & Ripley MASS book, Editions 4 and earlier, provide a very educational and useful discussion of analyses of mixture models beyond the fitting considerations (which are nicely covered as well). I do not have my book copy with me at the moment, but I believe in the 4th edition the material is covered in the last chapter entitled "Optimization". Hope that Helps. Best Regards, Bill ---------------------------------------- Bill Pikounis, Ph.D. Biometrics Research Department Merck Research Laboratories PO Box 2000, MailDrop RY33-300 126 E. Lincoln Avenue Rahway, New Jersey 07065-0900 USA v_bill_pikounis at merck.com Phone: 732 594 3913 Fax: 732 594 1565
-----Original Message----- From: Joke Allemeersch [mailto:Joke.Allemeersch at esat.kuleuven.ac.be] Sent: Thursday, July 17, 2003 11:58 AM To: r-help at stat.math.ethz.ch Subject: [R] univariate normal mixtures Hello, I have a concrete statistical question: I have a sample of an univariate mixture of an unknown number (k) of normal distributions, each time with an unknown mean `m_i' and a standard deviation `k * m_i', where k is known factor constant for all the normal distributions. (The `i' is a subscript.) Is there a function in R that can estimate the number of normal distributions k and the means `m_i' for the different normal distributions from a sample? Or evt. a function that can estimate the `m_i', when the number of distributions `k' is known? So far I only found a package, called `normix'. But at first sight it only provides methods to sample from such distributions and to estimate the densities; but not to fit such a distribution. Can someone indicate where I can find an elegant solution? Thank you in advance Joke Allemeersch Katholieke universiteit Leuven. Belgium.
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