R: Maximum Likelihood Estimates for the Generalized Poisson...

generalized_poisson_likelihood {GPseq} | R Documentation

## Maximum Likelihood Estimates for the Generalized Poisson Model

### Description

This function calculates the Maximum Likelihood estimates for theta and lambda when vector y is fit to the Generalized Poisson Model. Newton Raphson Method is employed to calculate the MLE. The values are only valid if the Newton Raphson Method converges.

### Usage

    generalized_poisson_likelihood(y)

### Arguments

`y` | Vector of counts.

### Value

`mark` | 1 if the Newton Raphson Method converges. If mark = 0, then the values of theta and lambda are not applicable
`theta` | Maximum Likelihood Estimate for theta in the Generalized Poisson Model(theta,lambda)
`lambda` | Maximum Likelihood Estimate for lambda in the Generalized Poisson Model(theta,lambda)
`y_bar` | Mean of y which is also the Maximum Likelihood Estimate for lambda for the Poisson model
`length` | Length of y which will be later used to calculate the normalization values

### Author(s)

Sudeep Srivastava, Liang Chen

### References

Consul, P. C. (1989) Generalized Poisson Distributions: Properties and Applications. New York: Marcel Dekker.
Sudeep Srivastava, Liang Chen A two-parameter generalized Poisson model to improve the analysis of RNA-Seq data Nucleic Acids Research Advance Access published July 29,2010 doi : 10.1093/nar/gkq670

### Examples

    y = rpois(100,10);
    out = generalized_poisson_likelihood(y);
    #Check if it converged
    if(out$mark==1)
    {
    #Value of Theta
      cat("theta = ",out$theta,"lambda = ",out$lambda,"lambda_poisson = ",out$y_bar,"Length = ",out$length,"\n");
    }

[Package GPseq version 0.5 Index]