bernweibull {qmap} | R Documentation |
The Bernoulli-Weibull distribution
Description
Density, distribution function, quantile function and random
generation for the Bernoulli-Weibull distribution with parameters
prob
, shape
, and scale
.
Usage
dbernweibull(x, prob, scale, shape)
pbernweibull(q, prob, scale, shape)
qbernweibull(p, prob, scale, shape)
rbernweibull(n, prob, scale, shape)
Arguments
x , q |
vector of quantiles. |
p |
vector of probabilities. |
prob |
probability of non-zero event. |
n |
number of random samples. |
scale , shape |
shape and scale parameters of the weibull distribution. |
Details
Mixture of Bernoulli and Weibull distribution. The mixture is analogue
to the one described for the berngamma
distribution.
Value
dbernweibull
gives the density (pdf), pbernweibull
gives
the distribution function (cdf), qbernweibull
gives the
quantile function (inverse cdf), and rbernweibull
generates
random deviates.
Note
The implementation is largely based on the bweibull
family in
the CaDENCE
-package (Cannon, 2012) that was only available as
test version at time of implementation (Mar. 2012). The
CaDENCE
-package is available at
http://www.eos.ubc.ca/~acannon/CaDENCE/.
Author(s)
Lukas Gudmundsson
References
Cannon, A. J. Neural networks for probabilistic environmental prediction: Conditional Density Estimation Network Creation and Evaluation (CaDENCE) in R. Computers & Geosciences, 2012, 41, 126 - 135, doi:10.1016/j.cageo.2011.08.023.
See Also
Examples
data(obsprecip)
(ts <- startbernweibull(obsprecip[,1]))
hist(obsprecip[,1],freq=FALSE)
lines(seq(0,max(obsprecip[,1])),
dbernweibull(seq(0,max(obsprecip[,1])),
prob=ts$prob,
shape=ts$shape,
scale=ts$scale),
col="red")
pp <- seq(0.01,0.99,by=0.01)
qq <-quantile(obsprecip[,1],probs=pp)
plot(qq,pp)
lines(qbernweibull(pp,
prob=ts$prob,
scale=ts$scale,
shape=ts$shape),
pp,col="red")
plot(qq,pp)
lines(qq,
pbernweibull(qq,
prob=ts$prob,
scale=ts$scale,
shape=ts$shape),
col="red")
hist(rbernweibull(1000,prob=ts$prob,
shape=ts$shape,
scale=ts$scale),freq=TRUE)