I'll check it out, thanks a million Micheal! On 19 March 2012 19:59, R. Michael Weylandt <michael.weyla...@gmail.com> wrote: > Take a look at fitdistr in the MASS package. > > fitdistr(x, "normal") > > I don't think you need to supply start values for the normal since its > loglikelihood function is nicely behaved. You may need to for harder > distributions. > > Michael > > On Mon, Mar 19, 2012 at 2:54 PM, Vihan Pandey <vihanpan...@gmail.com> wrote: >> I see, that could be an option, however isn't there a fitting function >> which would do that on given data? >> >> On 19 March 2012 19:49, R. Michael Weylandt <michael.weyla...@gmail.com> >> wrote: >>> If I understand you correctly, a univariate Gaussian distribution is >>> uniquely determined by its first two moments so you can just fit those >>> directly (using sample mean for population mean and sample variance >>> with Besel's correction for population variance) and get the "best" >>> Gaussian (in a ML sense). >>> >>> E.g., >>> >>> x <- rnorm(500, 3, 2) >>> >>> hist(x, freq = FALSE) >>> lines(seq(min(x), max(x), length.out = 300) -> y, dnorm(y, mean(x), >>> sd(x)), col = 2) >>> >>> Hope this helps, >>> Michael >>> >>> On Mon, Mar 19, 2012 at 12:47 PM, Vihan Pandey <vihanpan...@gmail.com> >>> wrote: >>>> Hello, >>>> >>>> I am trying to fit my histogram to a smooth Gaussian curve(the data >>>> closely resembles one except a few bars). >>>> >>>> This is my code : >>>> >>>> #!/usr/bin/Rscript >>>> >>>> out_file = "irc_20M_opencl_test.png" >>>> png(out_file) >>>> >>>> scan("my.csv") -> myvals >>>> >>>> hist(myvals, breaks = 50, main = "My Distribution",xlab = "My Values") >>>> >>>> pdens <- density(myvals, na.rm=T) >>>> plot(pdens, col="black", lwd=3, xlab="My values", main="Default KDE") >>>> >>>> dev.off() >>>> >>>> print(paste("Plot was saved in:", getwd())) >>>> >>>> the problem here is that I a jagged distribution, you can see the result : >>>> >>>> http://s15.postimage.org/9ucmkx3bf/foobar.png >>>> >>>> this is the original histogram : >>>> >>>> http://s12.postimage.org/e0lfp7d5p/foobar2.png >>>> >>>> any ideas on how I can smoothen it to a Gaussian curve? >>>> >>>> Thanks, >>>> >>>> - vihan >>>> >>>> ______________________________________________ >>>> 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.
______________________________________________ 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.