gamlss.dist: ZIP – R documentation

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ZIP

Zero inflated poisson distribution for fitting a GAMLSS model

Description

The function ZIP defines the zero inflated Poisson distribution, a two parameter distribution, for a gamlss.family object to be used in GAMLSS fitting using the function gamlss(). The functions dZIP, pZIP, qZIP and rZIP define the density, distribution function, quantile function and random generation for the inflated poisson, ZIP(), distribution.

Usage

ZIP(mu.link = "log", sigma.link = "logit")
dZIP(x, mu = 5, sigma = 0.1, log = FALSE)
pZIP(q, mu = 5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
qZIP(p, mu = 5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
rZIP(n, mu = 5, sigma = 0.1)

Arguments

`mu.link`	defines the `mu.link`, with "log" link as the default for the `mu` parameter
`sigma.link`	defines the `sigma.link`, with "logit" link as the default for the sigma parameter which in this case is the probability at zero. Other links are "probit" and "cloglog"'(complementary log-log)
`x`	vector of (non-negative integer) quantiles
`mu`	vector of positive means
`sigma`	vector of probabilities at zero
`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]

Details

Let Y=0 with probability σ and Y \sim Po(μ) with probability (1-σ) the Y has a Zero inflated Poisson Distribution given by

sigma+(1-sigma)e^(-mu)

if (y=0)

f(y)=(1-sigma)e^-mu mu^y/y!

if (y>0) for y=0,1,...,.

Value

returns a gamlss.family object which can be used to fit a zero inflated poisson distribution in the gamlss() function.

Author(s)

Mikis Stasinopoulos, Bob Rigby

References

Lambert, D. (1992), Zero-inflated Poisson Regression with an application to defects in Manufacturing, Technometrics, 34, pp 1-14.

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 https://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, https://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.

Examples

ZIP()# gives information about the default links for the normal distribution
# creating data and plotting them 
dat<-rZIP(1000, mu=5, sigma=.1)
r <- barplot(table(dat), col='lightblue')
# library(gamlss)
# fit the distribution 
# mod1<-gamlss(dat~1, family=ZIP)# fits a constant for mu and sigma 
# fitted(mod1)[1]
# fitted(mod1,"sigma")[1]

gamlss.dist

Distributions for Generalized Additive Models for Location Scale and Shape

v5.3-2

GPL-2 | GPL-3

Authors

Mikis Stasinopoulos [aut, cre, cph], Robert Rigby [aut], Calliope Akantziliotou [ctb], Vlasios Voudouris [ctb], Gillian Heller [ctb], Fernanda De Bastiani [ctb], Raydonal Ospina [ctb], Nicoletta Motpan [ctb], Fiona McElduff [ctb], Majid Djennad [ctb], Marco Enea [ctb], Alexios Ghalanos [ctb], Christos Argyropoulos [ctb], Almond Stocker [ctb], Jens Lichter [ctb], Stanislaus Stadlmann [ctb]

Initial release

2021-03-08

ZIP