FWIW, the integral of a mixture density is the same mixture of the
CDFs, so you can use the pbeta functions:
pcustom <- function(x) (pbeta(x,2,6) + pbeta(x,6,2))/2
albyn
Quoting Gerhard :
Am Dienstag, 3. Januar 2012, 19:51:36 schrieb Prof. Dr. Matthias Kohl:
D <- AbscontDistribution(d = f
Am Dienstag, 3. Januar 2012, 19:51:36 schrieb Prof. Dr. Matthias Kohl:
> D <- AbscontDistribution(d = function(x) dbeta(x, 2, 6) + dbeta(x,6,2),
> low = 0, up = 1, withStand = TRUE)
Dear all,
thank you all again for your help.
So, summing up, (in case this might be useful to other beginners -
right. replace dbetas with pbetas.
albyn
Quoting Duncan Murdoch :
On 03/01/2012 1:33 PM, 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 quan
Am Dienstag, 3. Januar 2012, 08:50:44 schrieb VictorDelgado:
> VictorDelgado wrote
>
> > quantile(x)
>
> Correcting to
>
> quantile(q)
>
> -
Dear Victor,
thank you for your answer.
Best,
Gerhard
> Victor Delgado
> cedeplar.ufmg.br P.H.D. student
> www.fjp.mg.gov.br reseacher
> --
> Vie
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 19:3
On 03/01/2012 1:33 PM, 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.
You
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 :
Gerhard wrot
VictorDelgado wrote
>
>
> quantile(x)
>
>
Correcting to
quantile(q)
-
Victor Delgado
cedeplar.ufmg.br P.H.D. student
www.fjp.mg.gov.br reseacher
--
View this message in context:
http://r.789695.n4.nabble.com/calculate-quantiles-of-a-custom-function-tp4256887p4257575.html
Sent from the R
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
Am Dienstag, 3. Januar 2012, 11:05:11 schrieben Sie:
>
> The quick way is to look at the structure with 'str':
>
> str(integrate(myfunction,0,.9))
> List of 5
> $ value : num 1.85
> $ abs.error : num 2.05e-14
> $ subdivisions: int 1
> $ message : chr "OK"
> $ call: l
On Jan 3, 2012, at 7:24 AM, Gerhard wrote:
Hi,
I guess that my problem has an obvious answer, but I have not been
able to
find it.
Suppose I create a custom function, consisting of two beta-
distributions:
myfunction <- function(x) {
dbeta(x,2,6) + dbeta(x,6,2)
}
Given the symmetry
Gerhard:
Strictly speaking, it's quantiles of a custom "distribution", not function.
There may be some way to handle your example easily, but, in general,
you would need to solve the resulting integral equation. This is hard
-- closed form solutions rarely exist; good approximations require
work.
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