Description Usage Arguments Details Value Note Author(s) References See Also Examples
Density, distribution function, quantile function and random
generation for the Bernoulli-Log-Normal distribution with parameters
prob
, meanlog
, and sdlog
.
1 2 3 4 | dbernlnorm(x, prob, meanlog, sdlog)
pbernlnorm(q, prob, meanlog, sdlog)
qbernlnorm(p, prob, meanlog, sdlog)
rbernlnorm(n, prob, meanlog, sdlog)
|
x,q |
vector of quantiles. |
p |
vector of probabilities. |
prob |
probability of non-zero event. |
n |
number of random samples. |
meanlog,sdlog |
meanlog and sdlog parameters of the Log-Normal distribution. |
Mixture of Bernoulli and Log-Normal distribution. The mixture is analogue
to the one described for the berngamma
distribution.
dbernlnorm
gives the density (pdf), pbernlnorm
gives
the distribution function (cdf), qbernlnorm
gives the
quantile function (inverse cdf), and rbernlnorm
generates
random deviates.
The implementation is largely based on the blnorm
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/.
Lukas Gudmundsson
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | data(obsprecip)
(ts <- startbernlnorm(obsprecip[,1]))
hist(obsprecip[,1],freq=FALSE)
lines(seq(0,20),dbernlnorm(0:20,
prob=ts$prob,
meanlog=ts$meanlog,
sdlog=ts$sdlog),
col="red")
pp <- seq(0.01,0.99,by=0.01)
qq <-quantile(obsprecip[,1],probs=pp)
plot(qq,pp)
lines(qbernlnorm(pp,
prob=ts$prob,
meanlog=ts$meanlog,
sdlog=ts$sdlog),
pp,col="red")
plot(qq,pp)
lines(qq,
pbernlnorm(qq,
prob=ts$prob,
meanlog=ts$meanlog,
sdlog=ts$sdlog),
col="red")
hist(rbernlnorm(1000,prob=ts$prob,
meanlog=ts$meanlog,
sdlog=ts$sdlog),freq=FALSE)
|
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