ZeroModifiedNegativeBinomial {actuar} R Documentation

## The Zero-Modified Negative Binomial Distribution

### Description

Density function, distribution function, quantile function and random generation for the Zero-Modified Negative Binomial distribution with parameters `size` and `prob`, and arbitrary probability at zero `p0`.

### Usage

```dzmnbinom(x, size, prob, p0, log = FALSE)
pzmnbinom(q, size, prob, p0, lower.tail = TRUE, log.p = FALSE)
qzmnbinom(p, size, prob, p0, lower.tail = TRUE, log.p = FALSE)
rzmnbinom(n, size, prob, p0)
```

### Arguments

 `x` vector of (strictly positive integer) quantiles. `q` vector of quantiles. `p` vector of probabilities. `n` number of observations. If `length(n) > 1`, the length is taken to be the number required. `size` target for number of successful trials, or dispersion parameter. Must be positive, need not be integer. `prob` parameter. `0 < prob <= 1`. `p0` probability mass at zero. `0 <= p0 <= 1`. `log, log.p` logical; if `TRUE`, probabilities p are returned as log(p). `lower.tail` logical; if `TRUE` (default), probabilities are P[X ≤ x], otherwise, P[X > x].

### Details

The zero-modified negative binomial distribution with `size` = r, `prob` = p and `p0` = p0 is a discrete mixture between a degenerate distribution at zero and a (standard) negative binomial. The probability mass function is p(0) = p0 and

p(x) = (1-p0)/(1-p^r) f(x)

for x = 1, 2, …, r ≥ 0, 0 < p < 1 and 0 ≤ p0 ≤ 1, where f(x) is the probability mass function of the negative binomial. The cumulative distribution function is

P(x) = p0 + (1 - p0) [F(x) - F(0)]/[1 - F(0)].

The mean is (1-p0)m and the variance is (1-p0)v + p0(1-p0)m^2, where m and v are the mean and variance of the zero-truncated negative binomial.

In the terminology of Klugman et al. (2012), the zero-modified negative binomial is a member of the (a, b, 1) class of distributions with a = 1-p and b = (r-1)(1-p).

The special case `p0 == 0` is the zero-truncated negative binomial.

The limiting case `size == 0` is the zero-modified logarithmic distribution with parameters `1 - prob` and `p0`.

Unlike the standard negative binomial functions, parametrization through the mean `mu` is not supported to avoid ambiguity as to whether `mu` is the mean of the underlying negative binomial or the mean of the zero-modified distribution.

If an element of `x` is not integer, the result of `dzmnbinom` is zero, with a warning.

The quantile is defined as the smallest value x such that P(x) ≥ p, where P is the distribution function.

### Value

`dzmnbinom` gives the (log) probability mass function, `pzmnbinom` gives the (log) distribution function, `qzmnbinom` gives the quantile function, and `rzmnbinom` generates random deviates.

Invalid `size`, `prob` or `p0` will result in return value `NaN`, with a warning.

The length of the result is determined by `n` for `rzmnbinom`, and is the maximum of the lengths of the numerical arguments for the other functions.

### Note

Functions `{d,p,q}zmnbinom` use `{d,p,q}nbinom` for all but the trivial input values and p(0).

### Author(s)

Vincent Goulet vincent.goulet@act.ulaval.ca

### References

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

### See Also

`dnbinom` for the negative binomial distribution.

`dztnbinom` for the zero-truncated negative binomial distribution.

`dzmgeom` for the zero-modified geometric and `dzmlogarithmic` for the zero-modified logarithmic, which are special cases of the zero-modified negative binomial.

### Examples

```## Example 6.3 of Klugman et al. (2012)
p <- 1/(1 + 0.5)
dzmnbinom(1:5, size = 2.5, prob = p, p0 = 0.6)
(1-0.6) * dnbinom(1:5, 2.5, p)/pnbinom(0, 2.5, p, lower = FALSE) # same

## simple relation between survival functions
pzmnbinom(0:5, 2.5, p, p0 = 0.2, lower = FALSE)
(1-0.2) * pnbinom(0:5, 2.5, p, lower = FALSE) /
pnbinom(0, 2.5, p, lower = FALSE) # same

qzmnbinom(pzmnbinom(0:10, 2.5, 0.3, p0 = 0.1), 2.5, 0.3, p0 = 0.1)
```

[Package actuar version 3.1-4 Index]