The Cosine Sine Exponential family
Value
Returns a gamlss.family object which can be used to fit a CS2e distribution in the gamlss()
function.
Details
The Cosine Sine Exponential distribution with parameters mu
,
sigma
and nu
has density given by
\(f(x)=\frac{\pi \sigma \mu \exp(\frac{-x} {\nu})}{2 \nu [(\mu\sin(\frac{\pi}{2} \exp(\frac{-x} {\nu})) + \sigma\cos(\frac{\pi}{2} \exp(\frac{-x} {\nu}))]^2}, \)
for \(x > 0\), \(\mu > 0\), \(\sigma > 0\) and \(\nu > 0\).
References
Chesneau C, Bakouch HS, Hussain T (2018). “A new class of probability distributions via cosine and sine functions with applications.” Communications in Statistics-Simulation and Computation, 1–14.
Author
Johan David Marin Benjumea, johand.marin@udea.edu.co
Examples
# Example 1
# Generating some random values with
# known mu, sigma and nu
y <- rCS2e(n=100, mu = 0.1, sigma =1, nu=0.5)
# Fitting the model
require(gamlss)
mod <- gamlss(y~1, sigma.fo=~1, nu.fo=~1, family='CS2e',
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)
#> 0.1348761
exp(coef(mod, what='sigma'))
#> (Intercept)
#> 0.9997442
exp(coef(mod, what='nu'))
#> (Intercept)
#> 0.3470724
# Example 2
# Generating random values under some model
n <- 200
x1 <- runif(n, min=0.45, max=0.55)
x2 <- runif(n, min=0.4, max=0.6)
mu <- exp(0.2 - x1)
sigma <- exp(0.8 - x2)
nu <- 0.5
x <- rCS2e(n=n, mu, sigma, nu)
mod <- gamlss(x~x1, sigma.fo=~x2, nu.fo=~1,family=CS2e,
control=gamlss.control(n.cyc=50000, trace=FALSE))
coef(mod, what="mu")
#> (Intercept) x1
#> -3.417430 5.567052
coef(mod, what="sigma")
#> (Intercept) x2
#> 2.483353 -4.949813
exp(coef(mod, what="nu"))
#> (Intercept)
#> 0.422791