| InvGenExp | R Documentation |
Density, distribution function, quantile function and random
generation for the Inverse Generalized Exponential(IGE)
distribution with shape parameter alpha and scale parameter lambda.
dinv.genexp(x, alpha, lambda, log = FALSE)
pinv.genexp(q, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
qinv.genexp(p, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
rinv.genexp(n, alpha, lambda)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
alpha |
shape parameter. |
lambda |
scale parameter. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are
|
The Inverse Generalized Exponential(IGE) distribution has density
f(x; \alpha, \lambda) = \frac{\alpha \; \lambda}{x^2}\; e^{-\lambda /x} \; \left\{1-e^{-\lambda /x}\right\}^{\alpha - 1};\; (\alpha, \lambda) > 0, x > 0
where \alpha and \lambda are the shape and scale
parameters, respectively.
dinv.genexp gives the density,
pinv.genexp gives the distribution function,
qinv.genexp gives the quantile function, and
rinv.genexp generates random deviates.
Gupta, R. D. and Kundu, D. (2001). Exponentiated exponential family; an alternative to gamma and Weibull distributions, Biometrical Journal, 43(1), 117-130.
Gupta, R.D. and Kundu, D. (2007). Generalized exponential distribution: Existing results and some recent development, Journal of Statistical Planning and Inference. 137, 3537-3547.
.Random.seed about random number; sinv.genexp for Inverse Generalized Exponential(IGE) survival / hazard etc. functions
## Load data sets
data(repairtimes)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(repairtimes)
## Estimates of alpha & lambda using 'maxLik' package
## alpha.est = 1.097807, lambda.est = 1.206889
dinv.genexp(repairtimes, 1.097807, 1.206889, log = FALSE)
pinv.genexp(repairtimes, 1.097807, 1.206889, lower.tail = TRUE, log.p = FALSE)
qinv.genexp(0.25, 1.097807, 1.206889, lower.tail=TRUE, log.p = FALSE)
rinv.genexp(30, 1.097807, 1.206889)
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