ex2.sim{bivpois} |
R Documentation |
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
data(ex2.sim)
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 |
This data is used as
example two in Karlis and Ntzoufras (2004).
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.
pbivpois
, simple.bp
, lm.bp
, lm.dibp , ex1.sim , ex3.health , ex4.ita91 .
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