Dear Gerhard,
you could also use package "distr"; e.g.
library(distr)
## use generating function "AbscontDistribution"
D <- AbscontDistribution(d = function(x) dbeta(x, 2, 6) + dbeta(x,6,2),
low = 0, up = 1, withStand = TRUE)
## quantiles
q(D)(seq(0,1,0.1))
Best
Matthias
On 03.01.2012 19:33, Albyn Jones wrote:
What do quantiles mean here? If you have a mixture density, say
myf <- function(x,p0) p0*dbeta(x,2,6) + (1-p0)*dbeta(x,6,2)
then I know what quantiles mean. To find the Pth quantile use uniroot to
solve for the x such that myf(x,p0) - P =0.
albyn
Quoting VictorDelgado <victor.m...@fjp.mg.gov.br>:
Gerhard wrote
Suppose I create a custom function, consisting of two
beta-distributions:
myfunction <- function(x) {
dbeta(x,2,6) + dbeta(x,6,2)
}
How can I calculate the quantiles of myfunction?
Thank you in advance,
Gerhard
Gehard, if do you want to know the quantiles of the new distribution
created
by "myfunction". Maybe you can also do:
x <- seq(0,1,.01) # insert your 'x'
q <- myfunction(x)
# And:
quantile(x)
0% 25% 50% 75% 100%
0.000000 1.476177 2.045389 2.581226 2.817425
# This gives the sample quantiles. You can also look foward to
simulations
(like Bert Gunter had suggested) to know better the properties of
distributions quantiles obtained after 'myfunction'.
-----
Victor Delgado
cedeplar.ufmg.br P.H.D. student
www.fjp.mg.gov.br reseacher
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