dmzsm {sads} | R Documentation |

Density, distribution, quantile function and random generation
for Alonso & McKane's mZSM distribution with parameter `theta`

.

dmzsm(x, J, theta, log = FALSE) pmzsm(q, J, theta, lower.tail=TRUE, log.p=FALSE) qmzsm(p, J, theta, lower.tail = TRUE, log.p = FALSE) rmzsm(n, J, theta)

`x` |
vector of (non-negative integer) quantiles. In the context of species abundance distributions, this is a vector of abundance of species in a sample. |

`q` |
vector of (non-negative integer) quantiles. In the context of species abundance distributions, a vector of abundance of species in a sample. |

`n` |
number of random values to return. |

`p` |
vector of probabilities. |

`J` |
positive integer 0 < J < Inf, sample size. In the context of species abundance distributions, usually the number of individuals in a sample. |

`theta` |
positive real theta > 0; Hubbell's ‘fundamental biodiversity number’ |

`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 metacommunity Zero-sum multinomial distribution (mZSM) describes the probabilities of abundances (population sizes) in a random sample of size J taken from a collection of populations (the metacommunity). The total number of individuals in the metacommunity is fixed (zero-sum assumption). The populations in the metacommunity undergo a stochastic birth-death-immigration process, with equal demographic rates (neutrality or ecological equivalence assumption, Hubbell 2001). Alonso and McKane (2004) proposed an approximation for the density function for a large Poisson sample (J>100):

*p(x) = N(x)/(sum(from 1 to S) N(x))*

where S is the number of populations in the sample, and N(x) is the expected number of sampled populations of size x :

*N(x) = theta/x (1 - x/J)^(theta -1)*

Therefore, the mZSM is a model for species abundances distributions (SAD) in a sample taken from a community under the assumptions that (a) species abundances in the community follows the stationary distribution of a neutral, zero-sum stochastic process of birth, death and speciation (or migration); (b) sampling is a Poisson process with expected value well approximated by N(x), (c) individuals are sampled with replacement, or the fraction of total individuals sampled is small enough to approximate a sample with replacement.

`dmzsm`

gives the (log) density function, `pmzsm`

gives the (log)
distribution function, and `qmzsm`

gives the quantile function.

Invalid values for parameters `J`

or `theta`

will result in return
values `NaN`

, with a warning.

Paulo I Prado prado@ib.usp.br, Murilo Dantas Miranda and Andre Chalom

Alonso, D. and McKane, A.J. 2004. Sampling Hubbell's neutral
model of biodiversity. *Ecology Letters 7*:901-910.

Hubbell, S.P. 2001. *The Unified Neutral Theory of Biodiversity*.
Princeton University Press.

`fitmzsm`

for maximum likelihood estimation;
`alonso.eqn12`

in package untb which is based on the exact formulation of mZSM.

## Alonso & McKanne (2004) figure 2 data(moths) #Fisher's moths data m.tab <- hist(moths, breaks = 2^(0:12), plot = FALSE) plot(m.tab$density~m.tab$mids, log="xy", xlab = "Abundance", ylab = "Probability density", ylim=c(1e-7,1)) X <- 1:max(moths) Y <- dmzsm(X, J = sum(moths), theta = 39.8) lines(Y ~ X)

[Package *sads* version 0.4.2 Index]