I'll check it out, thanks a million Micheal!
On 19 March 2012 19:59, R. Michael Weylandt 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 n
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 wrote:
> I see, th
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 wrote:
> If I understand you correctly, a univariate Gaussian distribution is
> uniquely determined by its first two moments so you can just fit th
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
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 V
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