Multinomial

dmnom

rmnom

numeric vector; number of trials (zero or more).

size

\(k\)-column numeric matrix; probability of success on each trial.

prob

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

number of observations. If <code>length(n) &gt; 1</code>,
the length is taken to be the number required.

Probability mass function and random generation
for the multinomial distribution.

distribution

Density, distribution function, quantile function
and random generation for a number of univariate
and multivariate distributions. This package implements the
following distributions: Bernoulli, beta-binomial, beta-negative
binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
bivariate normal, bivariate Poisson, categorical, Dirichlet,
Dirichlet-multinomial, discrete gamma, discrete Laplace,
discrete normal, discrete uniform, discrete Weibull, Frechet,
gamma-Poisson, generalized extreme value, Gompertz,
generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
Laplace, location-scale t, logarithmic, Lomax, multivariate
hypergeometric, multinomial, negative hypergeometric,
non-standard beta, normal mixture, Poisson mixture, Pareto,
power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
slash, triangular, truncated binomial, truncated normal,
truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
zero-inflated negative binomial, zero-inflated Poisson.

Tymoteusz Wolodzko

extraDistr

Additional Univariate and Multivariate Distributions

Multinomial function

<dl><dt>x</dt>
<dd>\(k\)-column matrix of quantiles.</dd>
<dt>size</dt>
<dd>numeric vector; number of trials (zero or more).</dd>
<dt>prob</dt>
<dd>\(k\)-column numeric matrix; probability of success on each trial.</dd>
<dt>log</dt>
<dd>logical; if TRUE, probabilities p are given as log(p).</dd>
<dt>n</dt>
<dd>number of observations. If <code>length(n) &gt; 1</code>,
the length is taken to be the number required.</dd></dl>

Arguments

Multinomial distribution — Multinomial

<dl>

<dt>x</dt>
<dd>\(k\)-column matrix of quantiles.</dd>


<dt>size</dt>
<dd>numeric vector; number of trials (zero or more).</dd>


<dt>prob</dt>
<dd>\(k\)-column numeric matrix; probability of success on each trial.</dd>


<dt>log</dt>
<dd>logical; if TRUE, probabilities p are given as log(p).</dd>


<dt>n</dt>
<dd>number of observations. If <code>length(n) &gt; 1</code>,
the length is taken to be the number required.</dd>

</dl>

Multinomial: Multinomial distribution

Description

Usage

Arguments

Details

References

See Also

Examples