ex1.sim {bivpois}R Documentation

Bivpois Example 1 Dataset: Simulated Data

Description

The data has one pair (x,y) of bivariate Poisson variables and five variables (z1,…,z5) generated from N(0, 0.01) distribution. Hence Xi, Yi ~ BP( lambda_1i, lambda_2i, lambda_3i ) log(lambda_1i) = 1.8 + 2 Z1i + 3 Z3i log(lambda_2i) = 0.7 – Z1i – 3 Z3i + 3 Z5i log(lambda_3i) = 1.7 + Z1i – 2 Z2i + 2 Z3i – 2 Z4i

Usage

data(ex1.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(ex1.sim)          # load data of example 1
# -------------------------------------------------------------------------------
# Simple Bivariate Poisson Model
ex1.simple<-simple.bp( ex1.sim$x, ex1.sim$y ) # fit simple model of section 4.1.1
names(ex1.simple)      # monitor output variables
ex1.simple$lambda      # view lambda1 
ex1.simple$BIC         # view BIC
ex1.simple             # view all results of the model
#
# plot of loglikelihood vs. iterations
win.graph()
plot( 1:ex1.simple$iterations, ex1.simple$loglikelihood, xlab='Iterations',
ylab='Log-likelihood', type='l' )
 
# -------------------------------------------------------------------------------
# Fit Double and Bivariate Poisson models ()
#
# Model 2: DblPoisson(l1, l2)
ex1.m2<-lm.bp('x','y', y1y2~noncommon, data=ex1.sim, zeroL3=TRUE ) 
# Model 3: BivPoisson(l1, l2, l3); same as simple.bp(ex1.sim$x, ex1.sim$y)
ex1.m3<-lm.bp('x','y', y1y2~noncommon, data=ex1.sim )           
# Model 4: DblPoisson (l1=Full, l2=Full) 
ex1.m4<-lm.bp('x','y', y1y2~., data=ex1.sim, zeroL3=TRUE )
# Model 5: BivPoisson(l1=full, l2=full, l3=constant)
ex1.m5<-lm.bp('x','y', y1y2~., data=ex1.sim)
# Model 6: DblPois(l1,l2)
ex1.m6<-lm.bp('x','y', y1y2~noncommon+z1:noncommon+z3+I(z5*l2), data=ex1.sim, zeroL3=TRUE)
# Model 7: BivPois(l1,l2,l3=constant)
ex1.m7<-lm.bp('x','y', y1y2~noncommon+z1:noncommon+z3+I(z5*l2), data=ex1.sim)          
# Model 8: BivPoisson(l1=full, l2=full, l3=full)
ex1.m8<-lm.bp('x','y', y1y2~., y3~., data=ex1.sim)                                         
# Model 9: BivPoisson(l1=full, l2=full, l3=z1+z2+z3+z4)
ex1.m9<-lm.bp('x','y', y1y2~., y3~z1+z2+z3+z4, data=ex1.sim)                          
# Model 10: BivPoisson(l1, l2, l3=full)
ex1.m10<-lm.bp('x','y',y1y2~noncommon+z1:noncommon+z3+I(z5*l2), y3~., data=ex1.sim)   
# Model 11: BivPoisson(l1, l2, l3= z1+z2+z3+z4)
ex1.m11<-lm.bp('x','y',y1y2~noncommon+z1:noncommon+z3+I(z5*l2), y3~z1+z2+z3+z4, data=ex1.sim) 
#
ex1.m11$beta  # monitor all beta parameters of model 11
#
ex1.m11$beta1 # monitor all beta parameters of lambda1 of model 11
ex1.m11$beta2 # monitor all beta parameters of lambda2 of model 11
ex1.m11$beta3 # monitor all beta parameters of lambda3 of model 11

[Package bivpois version 0.43-1 Index]