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
>>>
>>> ______________________________________________
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>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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