Hi Michael, > Secondly, and perhaps more difficult, is a second data set. This, when > plotted as a histogram, has two clear peaks, perhaps even three, all of > which look as though they are normally distributed. So the theory is > that my data set is actually made up of two, possibly three, underlying > sub-sets of data which are normally distributed, but with different > means and standard deviations. So 1) how do I test for this? And 2) how > can I estimate the parameters (mean and SD) for the underlying > distributions?
The answer to 2, as pointed out already, is to use EMclust in package mclust. Testing for the presence of a mixture is difficult from a theoretical point of view, and as far as I know, nothing is already implemented in R. What you can do is: a) Let EMclust estimate the number of mixture components by BIC (it can also decide for only one component). b) Use a standard normality test such as shapiro.test to exclude homogeneous normality. This tells you that you have to fit something more complex than a single normal, but it does not tell you what. Christian > > Thanks in advance for your help > > Mick > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > *********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg [EMAIL PROTECTED], http://www.math.uni-hamburg.de/home/hennig/ ####################################################################### ich empfehle www.boag-online.de ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
