# Example 1
# Generating some random values with
# known mu, sigma and nu
# Will not be run this example because high number is cycles
# is needed in order to get good estimates
if (FALSE) {
y <- rEW(n=100, mu=2, sigma=1.5, nu=0.5)
# Fitting the model
require(gamlss)
mod <- gamlss(y~1, sigma.fo=~1, nu.fo=~1, family='EW',
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
# Will not be run this example because high number is cycles
# is needed in order to get good estimates
if (FALSE) {
n <- 200
x1 <- rpois(n, lambda=2)
x2 <- runif(n)
mu <- exp(2 + -3 * x1)
sigma <- exp(3 - 2 * x2)
nu <- 2
x <- rEW(n=n, mu, sigma, nu)
mod <- gamlss(x~x1, sigma.fo=~x2, nu.fo=~1, family=EW,
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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