| LFR | R Documentation |
Density, distribution function, quantile function and random
generation for the linear failure rate(LFR)
distribution with parameters alpha and beta.
dlfr(x, alpha, beta, log = FALSE)
plfr(q, alpha, beta, lower.tail = TRUE, log.p = FALSE)
qlfr(p, alpha, beta, lower.tail = TRUE, log.p = FALSE)
rlfr(n, alpha, beta)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
alpha |
parameter. |
beta |
parameter. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are
|
The linear failure rate(LFR) distribution has density
f(x) = \left(\alpha + \beta x\right)\; \exp\left\{-\left(\alpha x + \frac{\beta x^2}{2}\right)\right\};\, x \ge 0, \alpha > 0, \beta > 0.
where \alpha and \beta are the shape and scale
parameters, respectively.
dlfr gives the density,
plfr gives the distribution function,
qlfr gives the quantile function, and
rlfr generates random deviates.
Bain, L.J. (1974). Analysis for the Linear Failure-Rate Life-Testing Distribution, Technometrics, 16(4), 551 - 559.
Lawless, J.F.(2003). Statistical Models and Methods for Lifetime Data, John Wiley and Sons, New York.
Sen, A. and Bhattacharya, G.K.(1995). Inference procedure for the linear failure rate mode, Journal of Statistical Planning and Inference, 46, 59-76.
.Random.seed about random number; slfr for linear failure rate(LFR) survival / hazard etc. functions
## Load data sets
data(sys2)
## Maximum Likelihood(ML) Estimates of alpha & beta for the data(sys2)
## Estimates of alpha & beta using 'maxLik' package
## alpha.est = 1.77773e-03, beta.est = 2.77764e-06
dlfr(sys2, 1.777673e-03, 2.777640e-06, log = FALSE)
plfr(sys2, 1.777673e-03, 2.777640e-06, lower.tail = TRUE, log.p = FALSE)
qlfr(0.25, 1.777673e-03, 2.777640e-06, lower.tail=TRUE, log.p = FALSE)
rlfr(30, 1.777673e-03, 2.777640e-06)
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