bzinbReg: The bivariate zero-inflated negative binomial regression.

In Hunyong/BZINB: Bivariate Zero-Inflated Negative Binomial Model Estimator

Description

the bivariate zero-inflated negative regression.

Usage

bzinbReg(x, ...)

expt.names

## S3 method for class 'formula'
bzinbReg(
  mu.formula,
  nu.formula = ~1,
  data,
  zero.inflation = c("full", "co-ZI", "ZINB-NB", "NB-ZINB", "BNB"),
  tol = 1e-08,
  maxiter = 50000,
  showFlag = FALSE,
  vcov = FALSE,
  initial = NULL
)

## S3 method for class 'bzinbReg'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

tol	tolerance for judging convergence. tol = 1e-8 by default.
maxiter	maximum number of iterations allowed. tol = 50000 by default.
showFlag	if TRUE, the updated parameter estimates for each iteration are printed out. If a positive integer, the updated parameter estimates for iterations greater than showFlag are printed out.
vcov	if TRUE, the variance-covariance matrix and information matrix are returned.
initial	starting value of param for EM algorithm, a vector of nine values.
y1, y2	a pair of bzinb random vectors. nonnegative integer vectors. If not integers, they will be rounded to the nearest integers.
n	number of observations.

Format

An object of class character of length 12.

Value

• the regression coefficients by the MLE of the BZINB regression model.

  • coefficients estimate and standard error of the BZINB parameters

  • lik log-likelihood of the maximum likelihood estimate

  • iter total number of iterations

  • info information matrix. Provided when vcov is TRUE.

  • vcov variance-covariance matrix. Provided when vcov is TRUE.

• lik.bzinb gives the log-likelihood of a set of parameters for a BZINB pair.

Author(s)

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

References

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

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22.

Examples

library(devtools)
document()
load_all()
set.seed(1)
dat <- rBzinbData()
bzinbReg(cbind(y1, y2) ~ ., ~ X1, data = dat, maxiter = 10)
print(bzinbReg(cbind(y1, y2) ~ ., ~ X1, data = dat, maxiter = 10))
bzinbReg(cbind(y1, y2) ~ ., ~ X1, data = dat, zero.inflation = "co-ZI", maxiter = 10)
bzinbReg(cbind(y1, y2) ~ ., ~ X1, data = dat, zero.inflation = "ZINB-NB", maxiter = 10)
bzinb(y1 = data1[,1], y2 = data1[,2], showFlag = FALSE)

Hunyong/BZINB documentation built on Dec. 15, 2020, 4:56 a.m.