WEI2 can be used to define the Weibull distribution, a two parameter distribution, for a
gamlss.family object to be used in GAMLSS fitting using the function gamlss().
This is the parameterization of the Weibull distribution usually used in proportional hazard models and is defined in details below.
[Note that the GAMLSS function WEI uses a
different parameterization for fitting the Weibull distribution.]
The functions dWEI2, pWEI2, qWEI2 and rWEI2 define the density, distribution function, quantile function and random
generation for the specific parameterization of the Weibull distribution.WEI2(mu.link = "log", sigma.link = "log")
dWEI2(x, mu = 1, sigma = 1, log = FALSE)
pWEI2(q, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE)
qWEI2(p, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE)
rWEI2(n, mu = 1, sigma = 1)mu.link, with "log" link as the default for the mu parameter, other links are "inverse" and "identity"sigma.link, with "log" link as the default for the sigma parameter, other link is the "inverse" and "identity"length(n) > 1, the length is
taken to be the number requiredWEI2() returns a gamlss.family object which can be used to fit a Weibull distribution in the gamlss() function.
dWEI2() gives the density, pWEI2() gives the distribution
function, qWEI2() gives the quantile function, and rWEI2()
generates random deviates. The latest functions are based on the equivalent R functions for Weibull distribution.WEI2 the estimated parameters mu and sigma can be highly correlated so it is advisable to use the
CG() method for fitting [as the RS() method can be veru slow in this situation.]WEI2 is given by
$$f(y|\mu,\sigma)= \sigma\mu y^{\sigma-1}e^{-\mu
y^{\sigma}}$$
for $y>0$, $\mu>0$ and $\sigma>0$.
The GAMLSS functions dWEI2, pWEI2, qWEI2, and rWEI2 can be used to provide the pdf, the cdf, the quantiles and
random generated numbers for the Weibull distribution with argument mu, and sigma.
[See the GAMLSS function WEI for a different parameterization of the Weibull.]gamlss.family, WEI,WEI3,WEI2()
dat<-rWEI(100, mu=.1, sigma=2)
hist(dat)
# library(gamlss)
# gamlss(dat~1, family=WEI2, method=CG())Run the code above in your browser using DataLab