EEG {RelDists}

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

for x > 0, \mu > 0 and \sigma > 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

Almalki SJ, Nadarajah S (2014). “Modifications of the Weibull distribution: A review.” Reliability Engineering & System Safety, 124, 32–55. doi:10.1016/j.ress.2013.11.010.

Adamidis K, Dimitrakopoulou T, Loukas S (2005). “On an extension of the exponential-geometric distribution.” Statistics & probability letters, 73(3), 259–269.

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")