qmap (version 1.0-4)

bernweibull: 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.

Value

dbernweibull gives the density (pdf), pbernweibull gives the distribution function (cdf), qbernweibull gives the quantile function (inverse cdf), and rbernweibull generates random deviates.

Details

Mixture of Bernoulli and Weibull distribution. The mixture is analogue to the one described for the berngamma distribution.

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

Weibull, berngamma

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)