| LogisExp | R Documentation |
Density, distribution function, quantile function and random
generation for the Logistic-Exponential(LE)
distribution with shape parameter alpha and scale parameter lambda.
dlogis.exp(x, alpha, lambda, log = FALSE)
plogis.exp(q, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
qlogis.exp(p, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
rlogis.exp(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 Logistic-Exponential(LE) distribution has density
f(x) = \frac{\lambda \; \alpha \; e^{\lambda x} \left(e^{\lambda x} -1\right)^{\alpha -1} }{\left\{1+\left(e^{\lambda x} -1\right)^{\alpha } \right\}^2 };\, x\ge 0,\; \alpha >0,\; \lambda >0.
where \alpha and \lambda are the shape and scale
parameters, respectively.
dlogis.exp gives the density,
plogis.exp gives the distribution function,
qlogis.exp gives the quantile function, and
rlogis.exp generates random deviates.
Lan, Y. and Leemis, L. M. (2008). The Logistic-Exponential Survival Distribution, Naval Research Logistics, 55, 252-264.
.Random.seed about random number; slogis.exp for ExpExt survival / hazard etc. functions
## Load data sets
data(bearings)
## Maximum Likelihood(ML) Estimates of alpha & lambda for the data(bearings)
## Estimates of alpha & lambda using 'maxLik' package
## alpha.est = 2.36754, lambda.est = 0.01059
dlogis.exp(bearings, 2.36754, 0.01059, log = FALSE)
plogis.exp(bearings, 2.36754, 0.01059, lower.tail = TRUE, log.p = FALSE)
qlogis.exp(0.25, 2.36754, 0.01059, lower.tail=TRUE, log.p = FALSE)
rlogis.exp(30, 2.36754, 0.01059)
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