ex2.sim {bivpois}R Documentation

Bivpois Example 2 Dataset: Simulated Data

Description

The data has one pair (x,y) of diagonal inflated bivariate Poisson variables and five variables (z1,…,z5) generated from N(0, 0.12) distribution. Hence Xi, Yi ~ DIBP( lambda_1i, lambda_2i, lambda_3i , p=0.30, Poisson(2) ) loglambda_1i = 1.8 + 2 Z1i + 3 Z3i loglambda_2i = 0.7 – Z1i – 3 Z3i + 3 Z5i loglambda_3i = 1.7 + Z1i – 2 Z2i + 2 Z3i – 2 Z4i

Usage

data(ex2.sim)

Format

A data frame with 100 observations on the following 7 variables.

x,y
Simulated Bivariate Poisson Variables used as response
z1,z2,z3,z4,z5
Simulated N(0,0.01) explanatory variables

Details

This data is used as example one in Karlis and Ntzoufras (2004).

Source

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

References

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.

Examples

library(bivpois) # load bivpois library
data(ex2.sim)    # load ex2.sim data from bivpois library
#
# formula for lambda1 and lamba2
form1 <- y1y2~noncommon + z1:noncommon + z3 + I(l2*z5)
# formula for lambda3 
form2 <- y3~z1+z2+z3+z4
#
# Model  1: BivPois
ex2.m1 <-lm.bp  ( 'x', 'y', form1, form2, data=ex2.sim)
# Model  2: Zero Inflated BivPois 
ex2.m2 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=0 )
# Model  3: Diagonal Inflated BivPois with DISCRETE(1) diagonal inflation distribution
ex2.m3 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=1 )
# Model  4: Diagonal Inflated BivPois with DISCRETE(2) diagonal inflation distribution
ex2.m4 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=2 )
# Model  5: Diagonal Inflated BivPois with DISCRETE(3) diagonal inflation distribution
ex2.m5 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=3 )
# Model  6: Diagonal Inflated BivPois with DISCRETE(4) diagonal inflation distribution
ex2.m6 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=4 )
# Model  7: Diagonal Inflated BivPois with DISCRETE(5) diagonal inflation distribution
ex2.m7 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=5 )
# Model  8: Diagonal Inflated BivPois with DISCRETE(6) diagonal inflation distribution
ex2.m8 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='discrete', jmax=6 )
# Model  9: Diagonal Inflated BivPois with POISSON diagonal inflation distribution
ex2.m9 <-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='poisson' )
# Model 10: Diagonal Inflated BivPois with GEOMETRIC diagonal inflation distribution
ex2.m10<-lm.dibp( 'x', 'y', form1, form2, data=ex2.sim, distribution='geometric' )
#
# printing parameters of model 7
ex2.m7$beta1
ex2.m7$beta2
ex2.m7$beta3
ex2.m7$p
ex2.m7$theta
#
# printing parameters of model 9
ex2.m9$beta1
ex2.m9$beta2
ex2.m9$beta3
ex2.m9$p
ex2.m9$theta

[Package bivpois version 0.43-1 Index]