[R] fitting a histogram to a Gaussian curve
vihanpandey at gmail.com
Mon Mar 19 19:54:13 CET 2012
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.weylandt at 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).
> 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,
> On Mon, Mar 19, 2012 at 12:47 PM, Vihan Pandey <vihanpandey at gmail.com> wrote:
>> 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 :
>> out_file = "irc_20M_opencl_test.png"
>> 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")
>> print(paste("Plot was saved in:", getwd()))
>> the problem here is that I a jagged distribution, you can see the result :
>> this is the original histogram :
>> any ideas on how I can smoothen it to a Gaussian curve?
>> - vihan
>> R-help at r-project.org mailing list
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
More information about the R-help