Description Usage Arguments Details Value Note Author(s) References See Also Examples

The `BI()`

function defines the binomial distribution, a one parameter family distribution, for a `gamlss.family`

object to be used
in GAMLSS fitting using the function `gamlss()`

.
The functions `dBI`

, `pBI`

, `qBI`

and `rBI`

define the density, distribution function, quantile function and random
generation for the binomial, `BI()`

, distribution.

1 2 3 4 5 |

`mu.link` |
Defines the |

`x` |
vector of (non-negative integer) quantiles |

`mu` |
vector of positive probabilities |

`bd` |
vector of binomial denominators |

`p` |
vector of probabilities |

`q` |
vector of quantiles |

`n` |
number of random values to return |

`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] |

Definition file for binomial distribution.

*f(y|mu)=(Gamma(n+1)*Gamma(y+1)/Gamma(n-y+1))* mu^y *(1-mu)^(n-y)*

for *y=0,1,2,...,n* and *0<μ< 1*.

returns a `gamlss.family`

object which can be used to fit a binomial distribution in the `gamlss()`

function.

The response variable should be a matrix containing two columns, the first with the count of successes and the second with the count of failures.
The parameter `mu`

represents a probability parameter with limits *0 < mu <1*.
*n*mu* is the mean of the distribution where n is the binomial denominator.

Mikis Stasinopoulos, Bob Rigby and Calliope Akantziliotou

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019)
*Distributions for modeling location, scale, and shape: Using GAMLSS in R*, Chapman and Hall/CRC. An older version can be found in http://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
*Flexible Regression and Smoothing: Using GAMLSS in R*, Chapman and Hall/CRC.

1 2 3 4 5 6 7 8 | ```
BI()# gives information about the default links for the Binomial distribution
# data(aep)
# library(gamlss)
# h<-gamlss(y~ward+loglos+year, family=BI, data=aep)
# plot of the binomial distribution
curve(dBI(x, mu = .5, bd=10), from=0, to=10, n=10+1, type="h")
tN <- table(Ni <- rBI(1000, mu=.2, bd=10))
r <- barplot(tN, col='lightblue')
``` |

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