Dear Brian, I can suggest you to use density() function to get an estimate of the pdf you're finding (I believe it's unknown). Then you can plot the point you got by density() using plot(). In this way you have a graphic representation of you unknown pdf. According its shape and helping by the graphic you could try to understand what kind of pdf it would be (normal, gamma, weibul, etc.) After you can estimate parameters of pdf using your data with LS or ML methods. Then you can calculate the goodness of fit for each model of pdf and use the best one.
I hope I get you a little help. Cordially Vito Ricci [EMAIL PROTECTED] wrote: Hi there, Sorry if this is a rather loing post. I have a simple list of single feature data points from which I would like to generate a probability that an unseen point comes from the same distribution. To do this I am trying to estimate the probability density of the list of points and use this to generate a probability for the new unseen points. I have managed to use the R density function to generate the density estimate but have not been able to do anything with this - i.e. generate a rpobability that a new point comes from the same distribution. Is there a function to do this, or am I way off the mark using the density function at all? Thanks in advance, Brian. ===== Diventare costruttori di soluzioni Visitate il portale http://www.modugno.it/ e in particolare la sezione su Palese http://www.modugno.it/archivio/cat_palese.shtml ___________________________________ http://it.seriea.fantasysports.yahoo.com/ ______________________________________________ [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
