# bzip.b: The bivariate zero-inflated Poisson distribution (B) In bzinb: Bivariate Zero-Inflated Negative Binomial Model Estimator

## Description

random generation (`rbzip.b`), maximum likelihood estimation (`bzip.b`), and log-likelihood. (`lik.bzip.b`) for the bivariate zero-inflated Poisson (B) distribution with parameters equal to `(m0, m1, m2, p1, p2, p3, p4)`.

## Usage

 ```1 2 3 4 5 6``` ```lik.bzip.b(xvec, yvec, m0, m1, m2, p1, p2, p3, p4, param = NULL) rbzip.b(n, m0, m1, m2, p1, p2, p3, p4, param = NULL) bzip.b(xvec, yvec, tol = 1e-06, initial = NULL, showFlag = FALSE, maxiter = 200) ```

## Arguments

 `xvec, yvec` a pair of BZIP (B) random vectors. nonnegative integer vectors. If not integers, they will be rounded to the nearest integers. `m0, m1, m2` mean parameters of the Poisson variables. 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 (`(m0, m1, m2, p1, p2, p3, p4)`). Either `param` or individual parameters (`m0, m1, m2, p1, p2, p3, p4`) need to be provided. `n` number of observations. `tol` tolerance for judging convergence. `tol = 1e-8` by default. `initial` starting value of param for EM algorithm, a vector of nine values. `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. `maxiter` maximum number of iterations allowed. `tol = 50000` by default.

## Value

• `rbzip.b` gives a pair of random vectors following BZIP (B) distribution.

• `bzip.b` gives the maximum likelihood estimates of a BZIP (B) pair.

• `lik.bzip.b` gives the log-likelihood of a set of parameters for a BZIP (B) pair.

## Author(s)

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

 ``` 1 2 3 4 5 6 7 8 9 10``` ```# generating a pair of random vectors set.seed(1) data1 <- rbzip.b(n = 20, m0 = 1, m1 = 1, m2 = 1, p1 = 0.5, p2 = 0.2, p3 = 0.2, p4 = 0.1) lik.bzip.b(xvec = data1[, 1], yvec = data1[ ,2], m0 = 1, m1 = 1, m2 = 1, p1 = 0.5, p2 = 0.2, p3 = 0.2, p4 = 0.1) bzip.b(xvec = data1[,1], yvec = data1[,2], showFlag = FALSE) ```

bzinb documentation built on Dec. 8, 2019, 9:12 a.m.