# bzinb.se: The bivariate zero-inflated negative binomial distribution -... In bzinb: Bivariate Zero-Inflated Negative Binomial Model Estimator

 bzinb.se R Documentation

## 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, ...)
```

### 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`.

### 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.

### 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

```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))

```

bzinb documentation built on Oct. 30, 2022, 1:05 a.m.