KumIW | R Documentation |
The Kumaraswamy Inverse Weibull family
KumIW(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 Kumaraswamy Inverse Weibull Distribution with parameters mu
,
sigma
and nu
has density given by
f(x)= \mu \sigma \nu x^{-\sigma - 1} \exp{- \mu x^{-\sigma}} (1 - \exp{- \mu x^{-\sigma}})^{\nu - 1},
for x > 0
, \mu > 0
, \sigma > 0
and \nu > 0
.
The KumIW distribution with \nu=1
corresponds with the IW distribution.
Returns a gamlss.family object which can be used to fit a KumIW distribution in the gamlss()
function.
Freddy Hernandez, fhernanb@unal.edu.co
almalki2014modificationsRelDists
\insertRefshahbaz2012kumaraswamyRelDists
dKumIW
# Example 1
# Generating some random values with
# known mu, sigma and nu
y <- rKumIW(n=100, mu=1.5, sigma=2.3, nu=1)
# Fitting the model
require(gamlss)
mod <- gamlss(y~1, sigma.fo=~1, nu.fo=~1, family=KumIW,
control=gamlss.control(n.cyc=5000, trace=FALSE))
# Extracting the fitted values for mu, sigma and nu
# using the inverse link function
exp(coef(mod, what="mu"))
exp(coef(mod, what="sigma"))
exp(coef(mod, what="nu"))
# Example 2
# Generating random values under some model
n <- 200
x1 <- runif(n)
x2 <- runif(n)
mu <- exp(1 + -1 * x1)
sigma <- exp(1 + -1 * x2)
nu <- 5
y <- rKumIW(n=n, mu=mu, sigma=sigma, nu=nu)
mod <- gamlss(y~x1, sigma.fo=~x2, nu.fo=~1, family=KumIW,
control=gamlss.control(n.cyc=5000, trace=FALSE))
coef(mod, what="mu")
coef(mod, what="sigma")
exp(coef(mod, what="nu"))
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