| MOEW | R Documentation |
Density, distribution function, quantile function and random
generation for the Marshall-Olkin Extended Weibull (MOEW)
distribution with tilt parameter alpha and scale parameter lambda.
dmoew(x, alpha, lambda, log = FALSE)
pmoew(q, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
qmoew(p, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
rmoew(n, alpha, lambda)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
alpha |
shape parameter. |
lambda |
tilt parameter. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are
|
The Marshall-Olkin extended Weibull (MOEW) distribution has density
f(x) = \frac{{\lambda \alpha x^{\alpha - 1} \exp\left({-x^\alpha}\right)}}{{\left\{{1 - (1 - \lambda)\;\exp\left({-x^\alpha}\right)}\right\}^2}};\, x > 0, \lambda > 0, \alpha > 0
where \alpha and \lambda are the tilt and scale
parameters, respectively.
dmoew gives the density,
pmoew gives the distribution function,
qmoew gives the quantile function, and
rmoew generates random deviates.
Marshall, A. W., Olkin, I. (1997). A new method for adding a parameter to a family of distributions with application to the Weibull and Weibull families. Biometrika,84(3):641-652.
Marshall, A. W., Olkin, I.(2007). Life Distributions: Structure of Nonparametric, Semiparametric, and Parametric Families. Springer, New York.
.Random.seed about random number; smoew for MOEW survival / hazard etc. functions;
## Load data sets
data(sys2)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(sys2)
## alpha.est = 0.3035937, lambda.est = 279.2177754
dmoew(sys2, 0.3035937, 279.2177754, log = FALSE)
pmoew(sys2, 0.3035937, 279.2177754, lower.tail = TRUE, log.p = FALSE)
qmoew(0.25, 0.3035937, 279.2177754, lower.tail=TRUE, log.p = FALSE)
rmoew(50, 0.3035937, 279.2177754)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.