| BurrX | R Documentation |
Density, distribution function, quantile function and random
generation for the BurrX
distribution with shape parameter alpha and scale parameter lambda.
dburrX(x, alpha, lambda, log = FALSE)
pburrX(q, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
qburrX(p, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
rburrX(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 BurrX distribution has density
f(x; \alpha, \lambda) = 2 \alpha \lambda^2 x e^{-(\lambda x)^2} \left\{1-e^{-(\lambda x)^2} \right\}^{\alpha -1}; (\alpha, \lambda) > 0, x >0.
where \alpha and \lambda are the shape and scale
parameters, respectively.
dburrX gives the density,
pburrX gives the distribution function,
qburrX gives the quantile function, and
rburrX generates random deviates.
Kundu, D., and Raqab, M.Z. (2005). Generalized Rayleigh Distribution: Different Methods of Estimation, Computational Statistics and Data Analysis, 49, 187-200.
Surles, J.G., and Padgett, W.J. (2005). Some properties of a scaled Burr type X distribution, Journal of Statistical Planning and Inference, 128, 271-280.
Raqab, M.Z., and Kundu, D. (2006). Burr Type X distribution: revisited, Journal of Probability and Statistical Sciences, 4(2), 179-193.
.Random.seed about random number; sburrX for BurrX 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 = 1.1989515, lambda.est = 0.0130847
dburrX(bearings, 1.1989515, 0.0130847, log = FALSE)
pburrX(bearings, 1.1989515, 0.0130847, lower.tail = TRUE, log.p = FALSE)
qburrX(0.25, 1.1989515, 0.0130847, lower.tail=TRUE, log.p = FALSE)
rburrX(30, 1.1989515, 0.0130847)
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