iee {weightedCL}

INDEPENDENT ESTIMATING EQUATIONS FOR BINARY AND COUNT REGRESSION

Description

Independent estimating equations for binary and count regression.

Usage

iee(xdat,ydat,margmodel,link) 

Arguments

xdat

The d\times p matrix of covariates, where d is the length of the time-series and p is the number of covariates including the unit first column to account for the intercept (except for ordinal regression where there is no intercept).

ydat

The d-dimensional vector of dicrete time series reponse, where d is the length of the series.

margmodel

Indicates the marginal model. Choices are “poisson” for Poisson, “bernoulli” for Bernoulli, and “nb1” , “nb2” for the NB1 and NB2 parametrization of negative binomial in Cameron and Trivedi (1998).

link

The link function. Choices are “log” for the log link function, “logit” for the logit link function, and “probit” for the probit link function.

Details

The univariate parameters are estimated from the sum of univariate marginal log-likelihoods.

Value

A list containing the following components:

coef

The vector with the estimated regression parameters.

gam

The vector with the estimated parameters that are not regression parameters. This is NULL for Poisson and binary regression.

Author(s)

Aristidis K. Nikoloulopoulos [e-mail]

References

Cameron, A. C. and Trivedi, P. K. (1998) Regression Analysis of Count Data. Cambridge: Cambridge University Press.

Examples

################################################################################
#                      NB2 regression for count time-series data
################################################################################
################################################################################
#                      read and set up data set
################################################################################
data(polio)
ydat <-polio
d=length(ydat)
tvec=1:length(ydat)
tvec1=tvec-73
xdat <- cbind(1, tvec1/1000, cos(2 * pi * tvec1 / 12), sin(2 * pi * tvec1 / 12),
                      cos(2 * pi * tvec1 / 6), sin(2 * pi * tvec1 / 6)) 
################################################################################
#                      select the marginal model
################################################################################
margmodel="nb2"

i.est<-iee(xdat,ydat,margmodel)
cat("\niest: IEE estimates\n")
print(c(i.est$reg,i.est$gam))