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( λ1i, λ2i, λ3i  , p=0.30,  Poisson(2) )

logλ1i = 1.8 + 2 Z1i + 3 Z3i

logλ2i = 0.7 – Z1i – 3 Z3i + 3 Z5i

logλ3i = 1.7 + Z1i – 2 Z2i + 2 Z3i – 2 Z4i

Usage

data(ex2.sim)

Format

Dataframe with seven variables of length 100.

No

Name

Description

1

x,y

Simulated diagonal inflated Bivariate Poisson Variables used as response

2

z1, z2, z3, z4, z5

Simulated N(0,0.12) Explanatory variables used as response

Details

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

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.

See Also

pbivpois,  simple.bp , lm.bp, lm.dibp , ex1.sim , ex3.health , ex4.ita91 .

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

 


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