Loglogistic: The Loglogistic Distribution

LoglogisticR Documentation

The Loglogistic Distribution


Density function, distribution function, quantile function, random generation, raw moments and limited moments for the Loglogistic distribution with parameters shape and scale.


dllogis(x, shape, rate = 1, scale = 1/rate, log = FALSE)
pllogis(q, shape, rate = 1, scale = 1/rate,
        lower.tail = TRUE, log.p = FALSE)
qllogis(p, shape, rate = 1, scale = 1/rate,
        lower.tail = TRUE, log.p = FALSE)
rllogis(n, shape, rate = 1, scale = 1/rate)
mllogis(order, shape, rate = 1, scale = 1/rate)
levllogis(limit, shape, rate = 1, scale = 1/rate,
          order = 1)


x, q

vector of quantiles.


vector of probabilities.


number of observations. If length(n) > 1, the length is taken to be the number required.

shape, scale

parameters. Must be strictly positive.


an alternative way to specify the scale.

log, log.p

logical; if TRUE, probabilities/densities p are returned as \log(p).


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


order of the moment.


limit of the loss variable.


The loglogistic distribution with parameters shape = \gamma and scale = \theta has density:

f(x) = \frac{\gamma (x/\theta)^\gamma}{% x [1 + (x/\theta)^\gamma]^2}

for x > 0, \gamma > 0 and \theta > 0.

The kth raw moment of the random variable X is E[X^k], -\gamma < k < \gamma.

The kth limited moment at some limit d is E[\min(X, d)^k], k > -\gamma and 1 - k/\gamma not a negative integer.


dllogis gives the density, pllogis gives the distribution function, qllogis gives the quantile function, rllogis generates random deviates, mllogis gives the kth raw moment, and levllogis gives the kth moment of the limited loss variable.

Invalid arguments will result in return value NaN, with a warning.


levllogis computes the limited expected value using betaint.

Also known as the Fisk distribution. See also Kleiber and Kotz (2003) for alternative names and parametrizations.

The "distributions" package vignette provides the interrelations between the continuous size distributions in actuar and the complete formulas underlying the above functions.


Vincent Goulet vincent.goulet@act.ulaval.ca and Mathieu Pigeon


Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

See Also

dpareto3 for an equivalent distribution with a location parameter.


exp(dllogis(2, 3, 4, log = TRUE))
p <- (1:10)/10
pllogis(qllogis(p, 2, 3), 2, 3)

## mean
mllogis(1, 2, 3)

## case with 1 - order/shape > 0
levllogis(10, 2, 3, order = 1)

## case with 1 - order/shape < 0
levllogis(10, 2/3, 3, order = 1)

actuar documentation built on Nov. 8, 2023, 9:06 a.m.