ReversalPowerLogistic {powdist}

The Reversal Power Logistic Distribution

Description

Density, distribution function, quantile function and random generation for the reversal power logistic distribution with parameters mu, sigma and lambda.

Usage

drplogis(x, lambda = 1, mu = 0, sigma = 1, log = FALSE)

prplogis(q, lambda = 1, mu = 0, sigma = 1, lower.tail = TRUE,
  log.p = FALSE)

qrplogis(p, lambda = 1, mu = 0, sigma = 1, lower.tail = TRUE,
  log.p = FALSE)

rrplogis(n, lambda = 1, mu = 0, sigma = 1)

Arguments

x, q

vector of quantiles.

lambda

shape parameter.

mu, sigma

location and scale parameters.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

lower.tail

logical; if TRUE (default), probabilities are P[X \le x ], otherwise, P[X > x].

p

vector of probabilities.

n

number of observations.

Details

The reversal power Logistic distribution has density

f(x)=\lambda \left [\frac{1}{1+e^{\left ( \frac{x-\mu}{\sigma} \right )} }\right ]^{\lambda-1}\left[\frac{ e^{-\left ( \frac{x-\mu}{\sigma} \right )} }{\sigma\left ( 1+e^{-\left ( \frac{x-\mu}{\sigma} \right )} \right )^2}\right], where -\infty<\mu<\infty is the location paramether, \sigma^2>0 the scale parameter and \lambda>0 the shape parameter.

References

Anyosa, S. A. C. (2017) Binary regression using power and reversal power links. Master's thesis in Portuguese. Interinstitutional Graduate Program in Statistics. Universidade de São Paulo - Universidade Federal de São Carlos. Available in https://repositorio.ufscar.br/handle/ufscar/9016.

Bazán, J. L., Torres -Avilés, F., Suzuki, A. K. and Louzada, F. (2017) Power and reversal power links for binary regressions: An application for motor insurance policyholders. Applied Stochastic Models in Business and Industry, 33(1), 22-34.

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, volume 1, chapter 16. Wiley, New York.

Nagler J. (1994) Scobit: an alternative estimator to logit and probit. American Journal Political Science, 38(1), 230-255.

Prentice, R. L. (1976) A Generalization of the probit and logit methods for dose-response curves. Biometrika, 32, 761-768.

Examples

drplogis(1, 1, 3, 4)
prplogis(1, 1, 3, 4)
qrplogis(0.2, 1, 3, 4)
rrplogis(5, 2, 3, 4)