RelDists (version 1.0.0)

EEG: The Extended Exponential Geometric family

Description

The Extended Exponential Geometric family

Usage

EEG(mu.link = "log", sigma.link = "log")

Value

Returns a gamlss.family object which can be used to fit a EEG distribution in the gamlss() function.

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.

Author

Johan David Marin Benjumea,

Details

The Extended Exponential Geometric distribution with parameters mu and sigma has density given by

\(f(x)= \mu \sigma \exp(-\mu x)(1 - (1 - \sigma)\exp(-\mu x))^{-2},\)

for \(x > 0\), \(\mu > 0\) and \(\sigma > 0\).

References

almalki2014modificationsRelDists

adamidis2005extensionRelDists

See Also

dEEG

Examples

# Generating some random values with
# known mu, sigma, nu and tau
y <- rEEG(n=100, mu = 1, sigma =1.5)

# Fitting the model
require(gamlss)

mod <- gamlss(y~1, sigma.fo=~1, family=EEG,
              control=gamlss.control(n.cyc=5000, trace=FALSE))

# Extracting the fitted values for mu, sigma, nu and tau
# using the inverse link function
exp(coef(mod, what='mu'))
exp(coef(mod, what='sigma'))

# Example 2
# Generating random values under some model
n <- 200
x1 <- runif(n, min=0.1, max=0.2)
x2 <- runif(n, min=0.1, max=0.15)
mu <- exp(0.75 - x1)
sigma <- exp(0.5 - x2)
x <- rEEG(n=n, mu, sigma)

mod <- gamlss(x~x1, sigma.fo=~x2, family=EEG,
              control=gamlss.control(n.cyc=5000, trace=FALSE))

coef(mod, what="mu")
coef(mod, what="sigma")