WP | R Documentation |
The Weibull Poisson family
WP(mu.link = "log", sigma.link = "log", nu.link = "log")
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. |
nu.link |
defines the nu.link, with "log" link as the default for the nu parameter. |
The Weibull Poisson distribution with parameters mu
,
sigma
and nu
has density given by
f(x) = \frac{\mu \sigma \nu e^{-\nu}} {1-e^{-\nu}} x^{\mu-1} \exp({-\sigma x^{\mu}+\nu \exp({-\sigma} x^{\mu}) }),
for x > 0
.
Returns a gamlss.family object which can be used to fit a WP distribution in the gamlss()
function.
Amylkar Urrea Montoya, amylkar.urrea@udea.edu.co
Lu, Wanbo, and Daimin Shi. "A new compounding life distribution: the Weibull–Poisson distribution." Journal of applied statistics 39.1 (2012): 21-38.
dWP
# Example 1
# Generating some random values with
# known mu, sigma and nu
y <- rWP(n=3000, mu=1.5, sigma=0.5, nu=0.5)
# Fitting the model
require(gamlss)
mod1 <- gamlss(y~1, sigma.fo=~1, nu.fo=~1, family=WP,
control=gamlss.control(n.cyc=5000, trace=FALSE))
# Extracting the fitted values for mu, sigma and nu
# using the inverse link function
exp(coef(mod1, what="mu"))
exp(coef(mod1, what="sigma"))
exp(coef(mod1, what="nu"))
# Example 2
# Generating random values for a regression model
# A function to simulate a data set with Y ~ WP
gendat <- function(n) {
x1 <- runif(n)
x2 <- runif(n)
mu <- exp(-1.3 + 3.1 * x1)
sigma <- exp(0.9 - 3.2 * x2)
nu <- 0.5
y <- rWP(n=n, mu, sigma, nu)
data.frame(y=y, x1=x1, x2)
}
set.seed(1234)
dat <- gendat(n=100)
# Fitting the model
mod2 <- NULL
mod2 <- gamlss(y~x1, sigma.fo=~x2, nu.fo=~1,
family=WP, data=dat,
control=gamlss.control(n.cyc=5000, trace=FALSE))
coef(mod2, what="mu")
coef(mod2, what="sigma")
exp(coef(mod2, what="nu"))
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