VGAMdata: posnegbinUC – R documentation

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posnegbinUC

Positive-Negative Binomial Distribution

Description

Density, distribution function, quantile function and random generation for the positive-negative binomial distribution.

Usage

dposnegbin(x, size, prob = NULL, munb = NULL, log = FALSE)
pposnegbin(q, size, prob = NULL, munb = NULL,
           lower.tail = TRUE, log.p = FALSE)
qposnegbin(p, size, prob = NULL, munb = NULL)
rposnegbin(n, size, prob = NULL, munb = NULL)

Arguments

`x, q`	vector of quantiles.
`p`	vector of probabilities.
`n`	number of observations. Fed into `runif`.
`size, prob, munb, log`	Same arguments as that of an ordinary negative binomial distribution (see `dnbinom`). Some arguments have been renamed slightly. Short vectors are recycled. The parameter `1/size` is known as a dispersion parameter; as `size` approaches infinity, the negative binomial distribution approaches a Poisson distribution. Note that `prob` must lie in (0,1), otherwise a `NaN` is returned.
`log.p, lower.tail`	Same arguments as that of an ordinary negative binomial distribution (see `pnbinom`).

Details

The positive-negative binomial distribution is a negative binomial distribution but with the probability of a zero being zero. The other probabilities are scaled to add to unity. The mean therefore is

munb / (1-p(0))

where munb the mean of an ordinary negative binomial distribution.

Value

dposnegbin gives the density, pposnegbin gives the distribution function, qposnegbin gives the quantile function, and rposnegbin generates n random deviates.

Note

These functions are or are likely to be deprecated. Use Gaitnbinom instead.

Author(s)

T. W. Yee

References

Welsh, A. H., Cunningham, R. B., Donnelly, C. F. and Lindenmayer, D. B. (1996). Modelling the abundances of rare species: statistical models for counts with extra zeros. Ecological Modelling, 88, 297–308.

Examples

munb <- 5; size <- 4; n <- 1000
table(y <- rposnegbin(n, munb = munb, size = size))
mean(y)  # sample mean
munb / (1 - (size / (size + munb))^size)  # population mean
munb / pnbinom(0, mu = munb, size = size, lower.tail = FALSE)  # same as before

x <- (-1):17
(ii <- dposnegbin(x, munb = munb, size = size))
max(abs(cumsum(ii) - pposnegbin(x, munb = munb, size = size)))  # Should be 0

## Not run: 
x <- 0:10
barplot(rbind(dposnegbin(x, munb = munb, size = size),
                 dnbinom(x, mu   = munb, size = size)),
        beside = TRUE, col = c("blue","green"),
        main = paste("dposnegbin(munb = ", munb, ", size = ", size, ") (blue) vs",
                     " dnbinom(mu = ", munb, ", size = ", size, ") (green)", sep = ""),
        names.arg = as.character(x)) 
## End(Not run)

# Another test for pposnegbin()
nn <- 5000
mytab <- cumsum(table(rposnegbin(nn, munb = munb, size = size))) / nn
myans <- pposnegbin(sort(as.numeric(names(mytab))), munb = munb, size = size)
max(abs(mytab - myans))  # Should be 0

VGAMdata

Data Supporting the 'VGAM' Package

v1.1-5

GPL-2

Authors

Thomas Yee [aut, cre, cph], James Gray [dtc]

Initial release

2021-01-13