ex3.health {bivpois} | R Documentation |
Demand for health care in Australia data (Cameron and Trivedi, 1986). The data refer to the Australian Health survey for 1977-1978 with sample size equal to 5190.
data(ex3.health)
A data frame with 5190 observations on the following 20 variables.
Details can be found in Cameron and Trivedi (1986). This data is used as example three in Karlis and Ntzoufras (2005). In this illustration two variables are used as response: the number of consultations with a doctor or a specialist and the total number of prescribed medications used in past 2 days (doctorco, prescrib). Three variables have been used as covariates: the gender (1 female, 0 male), the age in years divided by 100 (measured as midpoints of age groups) and the annual income in Australian dollars divided by 1000 (measured as midpoint of coded ranges) {sex, age, income}.
Cameron, A.C. and Trivedi, P.K. (1986). Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests, Journal of Applied Econometrics, 1, 29 - 54.
1. Cameron, A.C., Trivedi, P.K., Milne, F. and Piggott, J. (1988). A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia, Review of Economic Studies, 55, 85 - 106.
2. Cameron, A.C. and Trivedi, P.K. (1993). Tests of Independence in Parametric Models with Applications and Illustrations, Journal of Business & Economics Statistics, 11, 29 - 43.
3. Karlis, D. and Ntzoufras, I. (2005). Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R. Journal of Statistical Software (to appear).
4. 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.
# Models of example 3 can be fitted using the command # demo(ex3, package='bivpois') # # Here we present the commands for the same models commented out in order to save time # #library(bivpois) #data(ex3.health) # Bivariate Poisson models #ex3.model.a<-lm.bp(doctorco~sex+age+income, prescrib~sex+age+income, # data=ex3.health) #ex3.model.b<-lm.bp(doctorco~sex+age+income, prescrib~sex+age+income, l3=~sex, # data=ex3.health) # Double Poisson model #ex3.model.c<-lm.bp(doctorco~sex+age+income, prescrib~sex+age+income, # data=ex3.health, zeroL3=TRUE) # # diagonal inflated models #ex3.dibp.a<-lm.dibp(doctorco~sex+age+income, prescrib~sex+age+income, # data=ex3.health, jmax=0) # model (a) #ex3.dibp.b<-lm.dibp(doctorco~sex+age+income, prescrib~sex+age+income,l3=~sex, # data=ex3.health, jmax=0) # model (b)