gamlss.countKinf (version 3.5.1)

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

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]

Value

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

Details

The definition for the K-inflated Waring distribution.

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

# NOT RUN {
#--------------------------------------------------------------------------------

# 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)
# }
# 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)
# }
# 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))
#--------------------------------------------------------------------------------
# }