bzinb (version 1.0.8)

bzinb.se: The bivariate zero-inflated negative binomial distribution - Standard error estimation

Description

Standard errors of the BZINB distribution parameter estimates are calculated based on maximum likelihood estimation. If param is NULL, the parameters are first estimated by bzinb function.

Usage

bzinb.se(xvec, yvec, a0, a1, a2, b1, b2, p1, p2, p3, p4, param = NULL, ...)

Value

Standard error of rho, logit.rho, a0, a1, a2, b1, b2, p1, p2, p3, and p4 estimates, variance-covariance matrix (vcov) and information matrix. See bzinb for more detail. iter is NA, if the param

is given.

Arguments

xvec, yvec

a pair of bzinb random vectors. nonnegative integer vectors. If not integers, they will be rounded to the nearest integers.

a0, a1, a2

shape parameters of the latent gamma variables. They must be positive.

b1, b2

scale parameters for the latent gamma variables. They must be positive.

p1, p2, p3, p4

proportions summing up to 1 (p1 + p2 + p3 + p4 = 1). p1 is the probability of both latent Poisson variables being observed. p2 is the probability of only the first Poisson variables being observed. p3 is the probability of only the second Poisson variables being observed, and p4 is the probability of both Poisson variables being dropped out.

param

a vector of parameters ((a0, a1, a2, b1, b2, p1, p2, p3, p4)). See bzinb for more detail.

...

Other arguments passed on to bzinb function, when param is NULL.

Author

Hunyong Cho, Chuwen Liu, Jinyoung Park, and Di Wu

References

Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation), "A bivariate zero-inflated negative binomial model for identifying underlying dependence"

Examples

set.seed(1)
data1 <- rbzinb(n = 20, a0 = 1, a1 = 1, a2 = 1, 
                b1 = 1, b2 = 1, p1 = 0.5, p2 = 0.2, 
                p3 = 0.2, p4 = 0.1)
bzinb.se(xvec = data1[,1], yvec = data1[,2], 
         param = c(5.5, 0.017, 0.017, 0.33, 0.36, 
                   0.53, 0.30, 0.08, 0.09))