The Inverse Paralogistic Distribution
Density function, distribution function, quantile function, random generation,
raw moments and limited moments for the Inverse Paralogistic
distribution with parameters shape
and scale
.
dinvparalogis(x, shape, rate = 1, scale = 1/rate, log = FALSE) pinvparalogis(q, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE) qinvparalogis(p, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE) rinvparalogis(n, shape, rate = 1, scale = 1/rate) minvparalogis(order, shape, rate = 1, scale = 1/rate) levinvparalogis(limit, shape, rate = 1, scale = 1/rate, order = 1)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
shape, scale |
parameters. Must be strictly positive. |
rate |
an alternative way to specify the scale. |
log, log.p |
logical; if |
lower.tail |
logical; if |
order |
order of the moment. |
limit |
limit of the loss variable. |
The inverse paralogistic distribution with parameters shape
= a and scale
= s has density:
f(x) = a^2 (x/s)^(a^2)/(x [1 + (x/s)^a]^(a + 1))
for x > 0, a > 0 and b > 0.
The kth raw moment of the random variable X is E[X^k], -shape^2 < k < shape.
The kth limited moment at some limit d is E[min(X, d)^k], k > -shape^2 and 1 - k/shape not a negative integer.
dinvparalogis
gives the density,
pinvparalogis
gives the distribution function,
qinvparalogis
gives the quantile function,
rinvparalogis
generates random deviates,
minvparalogis
gives the kth raw moment, and
levinvparalogis
gives the kth moment of the limited loss
variable.
Invalid arguments will result in return value NaN
, with a warning.
levinvparalogis
computes computes the limited expected value using
betaint
.
See 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.
exp(dinvparalogis(2, 3, 4, log = TRUE)) p <- (1:10)/10 pinvparalogis(qinvparalogis(p, 2, 3), 2, 3) ## first negative moment minvparalogis(-1, 2, 2) ## case with 1 - order/shape > 0 levinvparalogis(10, 2, 2, order = 1) ## case with 1 - order/shape < 0 levinvparalogis(10, 2/3, 2, order = 1)
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