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The Weibull Geometric distribution

Usage

WG(mu.link = "log", sigma.link = "log", nu.link = "logit")

Arguments

defines the mu.link, with "log" link as the default for the mu parameter.

defines the sigma.link, with "log" link as the default for the sigma.

defines the nu.link, with "log" link as the default for the nu parameter.

Value

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

Details

The weibull geometric distribution with parameters mu, sigma and nu has density given by

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

for \(x > 0\), \(\mu > 0\), \(\sigma > 0\) and \(0 < \nu < 1\).

References

Barreto-Souza W, de Morais AL, Cordeiro GM (2011). “The Weibull-geometric distribution.” Journal of Statistical Computation and Simulation, 81(5), 645–657.

See also

Author

Johan David Marin Benjumea, johand.marin@udea.edu.co

Examples

# Example 1
# Generating some random values with
# known mu, sigma and nu
y <- rWG(n=100,  mu = 0.9, sigma = 2, nu = 0.5)

# Fitting the model
require(gamlss)

mod <- gamlss(y~1, sigma.fo=~1, nu.fo=~1, family='WG',
              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'))
#> (Intercept) 
#>    2.047776 
exp(coef(mod, what='sigma'))
#> (Intercept) 
#>   0.9891653 
exp(coef(mod, what='nu'))
#> (Intercept) 
#>    1.439952 

# Example 2
# Generating random values under some model
n     <- 200
x1    <- runif(n)
x2    <- runif(n)
mu    <- exp(- 0.2 * x1)
sigma <- exp(1.2 - 1 * x2)
nu    <- 0.5
x     <- rWG(n=n, mu, sigma, nu)

mod <- gamlss(x~x1, mu.fo=~x1, sigma.fo=~x2, nu.fo=~1, family=WG,
              control=gamlss.control(n.cyc=50000, trace=FALSE))

coef(mod, what="mu")
#> (Intercept)          x1 
#>   0.9340655  -0.2575587 
coef(mod, what="sigma")
#> (Intercept)          x2 
#> -0.18783559  0.04320311 
coef(mod, what='nu')
#> (Intercept) 
#>  -0.6876997