KIWARING: K-inflated Waring distributions for fitting a GAMLSS model

In gamlss.countKinf: Generating and Fitting K-Inflated 'discrete gamlss.family' Distributions

Description

The function KIWARING defines the K-inflated Waring distribution, a three parameter distribution, for a gamlss.family object to be used in GAMLSS fitting using the function gamlss(). The functions dKIWARING, pKIWARING, qKIWARING and rKIWARING define the density, distribution function, quantile function and random generation for the K-inflated Waring, KIWARING(), distribution.

Usage

 KIWARING(mu.link = "log", sigma.link = "log", nu.link = "logit", kinf="K")

dKIWARING(x, mu = 1, sigma = 1, nu = 0.3, kinf=0 ,log = FALSE)

pKIWARING(q, mu = 1, sigma = 1, nu = 0.3, kinf=0, lower.tail = TRUE,
    log.p = FALSE)

qKIWARING(p, mu = 1, sigma = 1, nu = 0.3, kinf=0, lower.tail = TRUE,
    log.p = FALSE)

rKIWARING(n, mu = 1, sigma = 1, nu = 0.3, kinf=0)

Arguments

mu.link	Defines the mu.link, with "log" link as the default for the mu parameter
sigma.link	Defines the sigma.link, with "log" link as the default for the sigma parameter
nu.link	Defines the nu.link, with "logit" link as the default for the nu parameter
x	vector of (non-negative integer) quantiles
mu	vector of positive means
sigma	vector of positive despersion parameter
nu	vector of inflated point probability
p	vector of probabilities
q	vector of quantiles
n	number of random values to return
kinf	defines inflated point in generating K-inflated distribution
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

The definition for the K-inflated Waring distribution.

Value

The functions KIWARING return a gamlss.family object which can be used to fit K-inflated Waring distribution in the gamlss() function.

Author(s)

Saeed Mohammadpour <[e-mail]>, Mikis Stasinopoulos <[e-mail]>

References

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.

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.

Rigby, R. A. and Stasinopoulos D. M. (2010) The gamlss.family distributions, (distributed with this package or seehttp://www.gamlss.org/)

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.

Najafabadi, A. T. P. and MohammadPour, S. (2017). A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate-Making Systems. Asia-Pacific Journal of Risk and Insurance, 12.

See Also

gamlss.family, KIWARING

Examples

#--------------------------------------------------------------------------------

# gives information about the default links for the  Waring distribution
KIWARING()
#--------------------------------------------------------------------------------

# generate zero inflated Waring distribution
gen.Kinf(family=WARING, kinf=0)

# generate random sample from zero inflated Waring distribution
x<-rinf0WARING(1000,mu=1, sigma=.5, nu=.2)

# fit the zero inflated Waring distribution using gamlss
data<-data.frame(x=x)
## Not run: 
gamlss(x~1, family=inf0WARING, data=data)
histDist(x, family=inf0WARING)
## End(Not run)
#--------------------------------------------------------------------------------

# generated one inflated Waring distribution
gen.Kinf(family=WARING, kinf=1)

# generate random sample from one inflated Waring distribution
x<-rinf1WARING(1000,mu=1, sigma=.5, nu=.2)

# fit the one inflated Waring distribution using gamlss
data<-data.frame(x=x)
## Not run: 
gamlss(x~1, family=inf1WARING, data=data)
histDist(x, family=inf1WARING)
## End(Not run)
#--------------------------------------------------------------------------------

mu=4; sigma=.5; nu=.2;
par(mgp=c(2,1,0),mar=c(4,4,4,1)+0.1)

#plot the pdf using plot
plot(function(x) dinf1WARING(x, mu=mu, sigma=sigma, nu=nu), from=0, to=20,
n=20+1, type="h",xlab="x",ylab="f(x)",cex.lab=1.5)
#--------------------------------------------------------------------------------

#plot the cdf using plot
cdf <- stepfun(0:19, c(0,pinf1WARING(0:19, mu=mu, sigma=sigma, nu=nu)), f = 0)
plot(cdf, xlab="x", ylab="F(x)", verticals=FALSE, cex.points=.8, pch=16, main="",cex.lab=1.5)
#--------------------------------------------------------------------------------

#plot the qdf using plot
invcdf <- stepfun(seq(0.01,.99,length=19), qinf1WARING(seq(0.1,.99,length=20),mu, sigma), f = 0)
plot(invcdf, ylab=expression(x[p]==F^{-1}(p)), do.points=FALSE,verticals=TRUE,
     cex.points=.8, pch=16, main="",cex.lab=1.5, xlab="p")
#--------------------------------------------------------------------------------

# generate random sample
Ni <- rinf1WARING(1000, mu=mu, sigma=sigma, nu=nu)
 hist(Ni,breaks=seq(min(Ni)-0.5,max(Ni)+0.5,by=1),col="lightgray", main="",cex.lab=2)
barplot(table(Ni))
#--------------------------------------------------------------------------------

gamlss.countKinf documentation built on May 2, 2019, 2:10 p.m.