Hello,
You could do something like the following.
fun <- function(x, mean, sd1, sd2, p)
dnorm(x, mean, sd1)*p + dnorm(x, mean, sd2)*(1 - p)
fun2 <- function(x1, x2, mean, sd1, sd2, p){
p1 <- pnorm(x2, mean, sd1) - pnorm(x1, mean, sd1)
p2 <- pnorm(x2, mean, sd2) - pnorm(x1, mean, sd2)
p1*p + p2*(1 - p)
}
integrate(fun, 0, 1, mean = 0, sd1 = 1, sd2 = 2, p = 0.5)
fun2(0, 1, mean = 0, sd1 = 1, sd2 = 2, p = 0.5)
Hope this helps,
Rui Barradas
Em 30-01-2013 09:19, Johannes Radinger escreveu:
Hi,
I already found a conversation on the integration of a normal
distribution and two
suggested solutions
(https://stat.ethz.ch/pipermail/r-help/2007-January/124008.html):
1) integrate(dnorm, 0,1, mean = 0, sd = 1.2)
and
2) pnorm(1, mean = 0, sd = 1.2) - pnorm(0, mean = 0, sd = 1.2)
where the pnorm-approach is supposed to be faster and with higher precision.
I want to integrate a mixed normal distribution like:
normaldistr_1 * p + normaldistr_2 * (1-p)
where p is between 0 and 1 and the means for both distributions are 0
but the standard deviations differ.
In addition, I want to get the integrals from x to infinity or from -
infinity to x for
the mixed distribution.
Can that be done with high precision in R and if yes how?
best regards,
Johannes
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