On Sat, Mar 21, 2015 at 3:41 PM, Prof Brian Ripley
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 density function
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 N1
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
fitdis
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 leas
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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