EEG: The Extended Exponential Geometric family

In ousuga/RelDists: Estimation for some Reliability Distributions

The Extended Exponential Geometric family

Description

The Extended Exponential Geometric family

Usage

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

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.

Details

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

f(x)= μ σ \exp(-μ x)(1 - (1 - σ)\exp(-μ x))^{-2},

for x > 0, μ > 0 and σ > 0.

Value

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

Author(s)

Johan David Marin Benjumea, [e-mail]

References

\insertRef

almalki2014modificationsRelDists

\insertRef

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")

ousuga/RelDists documentation built on Jan. 12, 2023, 10:27 p.m.