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
>>
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