NegBinomial {stats} | R Documentation |

Density, distribution function, quantile function and random
generation for the negative binomial distribution with parameters
`size`

and `prob`

.

dnbinom(x, size, prob, mu, log = FALSE) pnbinom(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE) qnbinom(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE) rnbinom(n, size, prob, mu)

`x` |
vector of (non-negative 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 (the shape parameter of the gamma mixing distribution). Must be strictly positive. |

`prob` |
probability of success in each trial. `0 < prob <= 1` . |

`mu` |
alternative parametrization via mean: see Details |

`log, log.p` |
logical; if TRUE, probabilities p are given as log(p). |

`lower.tail` |
logical; if TRUE (default), probabilities are
P[X <= x], otherwise, P[X > x]. |

The negative binomial distribution with `size`

*= n* and
`prob`

*= p* has density

*p(x) = Gamma(x+n)/(Gamma(n) x!) p^n (1-p)^x*

for *x = 0, 1, 2, ...*, *n > 0* and *0 < p <= 1*.

This represents the number of failures which occur in a sequence of Bernoulli trials before a target number of successes is reached.

A negative binomial distribution can arise as a mixture of Poisson
distributions with mean distributed as a
*Γ* (`pgamma`

) distribution with scale parameter
`(1 - prob)/prob`

and shape parameter `size`

. (This
definition allows non-integer values of `size`

.)
In this model `prob`

= `scale/(1+scale)`

, and the mean is
`size * (1 - prob)/prob`

.

The alternative parametrization (often used in ecology) is by the
*mean* `mu`

, and `size`

, the *dispersion parameter*,
where `prob`

= `size/(size+mu)`

.
The variance is `mu + mu^2/size`

in this parametrization or
*n (1-p)/p^2* in the first one.

If an element of `x`

is not integer, the result of `dnbinom`

is zero, with a warning.

The quantile is defined as the smallest value *x* such that
*F(x) >= p*, where *F* is the distribution function.

`dnbinom`

gives the density,
`pnbinom`

gives the distribution function,
`qnbinom`

gives the quantile function, and
`rnbinom`

generates random deviates.

Invalid `size`

or `prob`

will result in return value
`NaN`

, with a warning.

`dnbinom`

computes via binomial probabilities, using code
contributed by Catherine Loader (see `dbinom`

).

`pnbinom`

uses `pbeta`

.

`qnbinom`

uses the Cornish–Fisher Expansion to include a skewness
correction to a normal approximation, followed by a search.

`rnbinom`

uses the derivation as a gamma mixture of Poissons, see

Devroye, L. (1986) *Non-Uniform Random Variate Generation.*
Springer-Verlag, New York. Page 480.

`dbinom`

for the binomial, `dpois`

for the
Poisson and `dgeom`

for the geometric distribution, which
is a special case of the negative binomial.

x <- 0:11 dnbinom(x, size = 1, prob = 1/2) * 2^(1 + x) # == 1 126 / dnbinom(0:8, size = 2, prob = 1/2) #- theoretically integer ## Cumulative ('p') = Sum of discrete prob.s ('d'); Relative error : summary(1 - cumsum(dnbinom(x, size = 2, prob = 1/2)) / pnbinom(x, size = 2, prob = 1/2)) x <- 0:15 size <- (1:20)/4 persp(x,size, dnb <- outer(x,size,function(x,s)dnbinom(x,s, pr= 0.4)), xlab = "x", ylab = "s", zlab="density", theta = 150) title(tit <- "negative binomial density(x,s, pr = 0.4) vs. x & s") image (x,size, log10(dnb), main= paste("log [",tit,"]")) contour(x,size, log10(dnb),add=TRUE) ## Alternative parametrization x1 <- rnbinom(500, mu = 4, size = 1) x2 <- rnbinom(500, mu = 4, size = 10) x3 <- rnbinom(500, mu = 4, size = 100) h1 <- hist(x1, breaks = 20, plot = FALSE) h2 <- hist(x2, breaks = h1$breaks, plot = FALSE) h3 <- hist(x3, breaks = h1$breaks, plot = FALSE) barplot(rbind(h1$counts, h2$counts, h3$counts), beside = TRUE, col = c("red","blue","cyan"), names.arg = round(h1$breaks[-length(h1$breaks)]))

[Package *stats* version 2.5.0 Index]