On Sat, Mar 21, 2015 at 3:41 PM, Prof Brian Ripley rip...@stats.ox.ac.uk
wrote:
On 21/03/2015 14:27, Johannes Radinger wrote:
Thanks for the fast response. The fitdistr() function works well for the
predefined density functions. However, what is the recommended approach
to optimize/fit a
On 21/03/2015 14:27, Johannes Radinger wrote:
Thanks for the fast response. The fitdistr() function works well for the
predefined density functions. However, what is the recommended approach
to optimize/fit a density function described by two superimposed normal
distributions? In my case it is
One way using the standard R distribution:
library(MASS)
?fitdistr
No optimization is needed to fit a normal distribution, though.
On 21/03/2015 13:05, Johannes Radinger wrote:
Hi,
I am looking for a way to fit data (vector of values) to a density function
using an optimization (ordinary
Thanks for the fast response. The fitdistr() function works well for the
predefined density functions. However, what is the recommended approach to
optimize/fit a density function described by two superimposed normal
distributions? In my case it is N1(mean=0,sd1)*p+N2(mean=0,sd2)*(1-p). With
Hi,
I am looking for a way to fit data (vector of values) to a density function
using an optimization (ordinary least squares or maximum likelihood fit).
For example if I have a vector of 100 values generated with rnorm:
rnorm(n=100,mean=500,sd=50)
How can I fit these data to a Gaussian density
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