R: The one-inflated beta distribution for fitting a GAMLSS

BEOI {gamlss.dist} | R Documentation

## The one-inflated beta distribution for fitting a GAMLSS

### Description

The function `BEOI()` defines the one-inflated beta distribution, a three parameter distribution, for a `gamlss.family` object to be used in GAMLSS fitting using the function `gamlss()`. The one-inflated beta is similar to the beta distribution but allows ones as y values. This distribution is an extension of the beta distribution using a parameterization of the beta law that is indexed by mean and precision parameters (Ferrari and Cribari-Neto, 2004). The extra parameter models the probability at one. The functions `dBEOI`, `pBEOI`, `qBEOI` and `rBEOI` define the density, distribution function, quantile function and random generation for the `BEOI` parameterization of the one-inflated beta distribution. `plotBEOI` can be used to plot the distribution. `meanBEOI` calculates the expected value of the response for a fitted model.

### Usage

    BEOI(mu.link = "logit", sigma.link = "log", nu.link = "logit")

    dBEOI(x, mu = 0.5, sigma = 1, nu = 0.1, log = FALSE)

    pBEOI(q, mu = 0.5, sigma = 1, nu = 0.1, lower.tail = TRUE, log.p = FALSE)

    qBEOI(p, mu = 0.5, sigma = 1, nu = 0.1, lower.tail = TRUE,
            log.p = FALSE)

    rBEOI(n, mu = 0.5, sigma = 1, nu = 0.1)

    plotBEOI(mu = .5, sigma = 1, nu = 0.1, from = 0.001, to = 1, n = 101,
        ...)

    meanBEOI(obj)

### Arguments

`mu.link` | the `mu` link function with default `logit`
`sigma.link` | the `sigma` link function with default `log`
`nu.link` | the `nu` link function with default `logit`
`x,q` | vector of quantiles
`mu` | vector of location parameter values
`sigma` | vector of precision parameter values
`nu` | vector of parameter values modelling the probability at one
`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]
`p` | vector of probabilities.
`n` | number of observations. If `length(n) > 1`, the length is taken to be the number required
`from` | where to start plotting the distribution from
`to` | up to where to plot the distribution
`obj` | a fitted `BEOI` object
`...` | other graphical parameters for plotting

### Details

The one-inflated beta distribution is given as

f(y)=nu

if (y=1)

f(y|mu,sigma)=(1-nu)*(Gamma(sigma)/Gamma(mu*sigma)*Gamma((1-mu)*sigma))*y^(mu*sigma-1)*(1-y)^(((1-mu)*sigma)-1)

if y=(0,1). The parameters satisfy 0<mu<1, sigma>0 and 0<nu<1.

Here E(y)=nu+(1-nu)*mu and Var(y)=(1-nu)*(mu*(1-mu))/(sigma+1) + nu*(1-nu)*(1-mu)^2.

### Value

returns a `gamlss.family` object which can be used to fit a one-inflated beta distribution in the `gamlss()` function.

### Note

This work is part of my PhD project at the University of Sao Paulo under the supervion of Professor Silvia Ferrari. My thesis is concerned with regression modelling of rates and proportions with excess of zeros and/or ones

### Author(s)

Raydonal Ospina, Department of Statistics, University of Sao Paulo, Brazil.

rospina@ime.usp.br

### References

Ferrari, S.L.P., Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31 (1), 799-815.

Ospina R. and Ferrari S. L. P. (2010) Inflated beta distributions, Statistical Papers, 23, 111-126.

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape (with discussion). Applied Statistics, 54 (3), 507-554.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006). Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files (see also http://www.gamlss.org/).

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.

### See Also

`gamlss.family`, `BEOI`

### Examples

    BEOI()# gives information about the default links for the BEOI distribution
    # plotting the distribution
    plotBEOI( mu =0.5 , sigma=5, nu = 0.1, from = 0.001, to=1, n = 101)
    # plotting the cdf
    plot(function(y) pBEOI(y, mu=.5 ,sigma=5, nu=0.1), 0.001, 0.999)
    # plotting the inverse cdf
    plot(function(y) qBEOI(y, mu=.5 ,sigma=5, nu=0.1), 0.001, 0.999)
    # generate random numbers
    dat<-rBEOI(100, mu=.5, sigma=5, nu=0.1)
    # fit a model to the data.
    # library(gamlss)
    #mod1<-gamlss(dat~1,sigma.formula=~1, nu.formula=~1, family=BEOI)
    #fitted(mod1)[1]
    #summary(mod1)
    #fitted(mod1,"mu")[1]        #fitted mu
    #fitted(mod1,"sigma")[1]     #fitted sigma
    #fitted(mod1,"nu")[1]        #fitted nu
    #meanBEOI(mod1)[1] # expected value of the response

[Package gamlss.dist version 5.0-2 Index]