Each of the two integrals (g1, g2) seem to be divergent (or at least is
considered to be so by R :) )
Try this:
z <- c(80, 20, 40, 30)
"f1" <- function(x, y, z) {dgamma(cumsum(z)[-length(z)], shape=x, rate=y)}
"g1" <- function(y, z) {integrate(function(x) {f1(x=x, y=y, z=z)}, 0.1,
0.5, rel.to
Thank you all, for the very helpful advice.
i want to estimate the parameters omega and beta of the gamma-nhpp-model
with numerical integration.
so one step in order to do this is to compute the normalizing constant,
but as you see below i get different values
## some reliability data, theses
More generally, if you want to do a two-dimensional integral, you will
do better to us a 2D integration algorithm, such as those in package
'adapt'.
Also, these routines are somewhat sensitive to scaling, so if the
correct answer is around 5e-9, you ought to rescale. You seem to be
in the f
On 24/01/2009 5:23 AM, Andreas Wittmann wrote:
Dear R useRs,
i have the function f1(x, y, z) which i want to integrate for x and y.
On the one hand i do this by first integrating for x and then for y, on
the other hand i do this the other way round and i wondering why i
doesn't get the same r
Dear R useRs,
i have the function f1(x, y, z) which i want to integrate for x and y.
On the one hand i do this by first integrating for x and then for y, on
the other hand i do this the other way round and i wondering why i
doesn't get the same result each way?
z <- c(80, 20, 40, 30)
"f1" <
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