simple.bp{bivpois}

R Documentation

Simple Bivariate Poisson Model

Description

Produces a "list" object which gives details regarding the fit of a simple bivariate Poisson model of the form (X,Y) ~ BP(ë1, ë2, ë3)

Usage

simple.bp(x, y, ini3=1.0, maxit=300, pres=1e-8)

Required Arguments

x, y

vectors containing the data

Optional Arguments

ini3

Initial value for ë3

maxit

Maximum number of EM steps

pres

Precision used in log-likelihood improvement

Value

A list object returned with the following variables.

lambda

Vector with parameter values ë1 , ë2, ë3

loglikelihood

Ìaximized log-likelihood of the fitted model. This is given in a vector form (one value per iteration).Using this we may monitor the log-likelihood improvement     and how EM algorithm works.

AIC, BIC

AIC and BIC of the model. Values are also given for the double Poisson model and the saturated model

parameters

Number of parameters

iterations

Number of iterations

Details

During the run of the algorithm the following details are printed: the iteration number, lambda1, lambda2, lambda3, the log-likelihood and the relative difference of the log-likelihood.

References

1.      Karlis, D. and Ntzoufras, I. (2004). Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in S. (submitted). Technical Report, Athens University of Economics and Business, Athens, Greece.

2.      Karlis, D. and Ntzoufras, I. (2003). Analysis of Sports Data Using Bivariate Poisson Models. Journal of the Royal Statistical Society, D, (Statistician), 52, 381 – 393.

Authors Information

1.      Dimitris Karlis, Department of Statistics, Athens University of Economics and Business, Athens, Greece, e-mail: karlis@aueb.gr .

2.      Ioannis Ntzoufras, Department of Statistics, Athens University of Economics and Business, Athens, Greece, e-mail: ntzoufras@aueb.gr  .

See Also

pbivpois,  lm.bp, lm.dibp .

Example

#

# Generation of BP(1,2,3) data

x3<-rpoisson(100, 3)

x1<-rpoisson(100, 1)+x3

x2<-rpoisson(100, 2)+x3

#

# fits the model

x<-simple.bp(x1, x2)

#

# Monitors parameters

x$lambda1

x$lambda2

x$lambda3

 


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