RStudio is only an IDE for R, and R is a programming language. It is surely possible in R, but that includes "programming".

Let the numeric vector x contain your data.

define the density, possibly as a weighted mix of a gaussian and a poisson:

dens 0, c>0 and 0

RStudio is only an IDE for R, and R is a programming language. It is surely possible in R, but that includes "programming".

Let the numeric vector x contain your data.

define the density, possibly as a weighted mix of a gaussian and a poisson:

dens 0, c>0 and 0

Thanks!

Dear Dmitry,

for the solutuion of such specific task I would like to reccomend you 

to address to Dr Alexey Pomerantsev who is avauilable on the RG site 

Best regards,

Alexey

Thanks! Probably I will contact him.

I'm not sure if original problem is correctly stated.  Gaussian distribution is continuous while Poisson's is discrete.  To put them on equal footing one has to discretize original data, say by producing a histogram.  Not only gaussian is no longer gaussian after such transformation but another question arises: what is the correct number of bins in resulting histogram?

I have to work further on the physical model and the assumptions, that led me to the Gaussian+Poisson approximation.

@Marek,

I had assumed that this case was similar to rolling dice. The value from the first die is distributed Uniform. The value for all other die follow the Uniform distribution (ideally, and no cheating). So I now take the sum of several dice. If I look at the distribution of the sum of enough dice I will get the Gaussian distribution.

I could do the same thing with random number generators. To get my first value I will take a number from a Gaussian distribution and a value from a Poisson distribution and add them together. I can do this over and over and plot a histogram of the results. At about 1,000,000 values one gets a clear picture of what the distribution looks like.

I agree that the result will be neither Gaussian nor Poisson.

@Timothy:

The sum of several dices (I mean fixed number of dices) still remains discrete (integer, always positive!), so how do you see a gaussian distribution here?  The same applies, by the way, to the average of fixed number of dices.  On the other hand, after random numbers are used, it is clear that for very narrow gaussians you will obtain something like 'noisy' Poisson distribution.  At this moment I have no idea about the case with wide gaussians but I would be happy to see the picture you described.