One way using the standard R distribution: library(MASS) ?fitdistr
No optimization is needed to fit a normal distribution, though. On 21/03/2015 13:05, Johannes Radinger wrote:
Hi, I am looking for a way to fit data (vector of values) to a density function using an optimization (ordinary least squares or maximum likelihood fit). For example if I have a vector of 100 values generated with rnorm: rnorm(n=100,mean=500,sd=50) How can I fit these data to a Gaussian density function to extract the mean and sd value of the underlying normal distribution. So the result should roughly meet the parameters of the normal distribution used to generate the data. The results will ideally be closer the true parameters the more data (n) are used to optimize the density function.
That's a concept called 'consistency' from the statistical theory of estimation. If you skipped that course, time to read up (but it is off-topic here).
-- Brian D. Ripley, rip...@stats.ox.ac.uk Emeritus Professor of Applied Statistics, University of Oxford 1 South Parks Road, Oxford OX1 3TG, UK ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
