Thanks that works. I am presuming that the density on the Y-axis would
be wrong in the case of a half-Cauchy distribution and in fact should be
doubled if it's folded at 0?
Chris
On 05/28/2010 09:02 AM, Uwe Ligges wrote:
Am 28.05.2010 15:29, schrieb Christopher David Desjardins:
Hi,
I am trying to recreate the right graph on page 524 of Gelman's 2006
paper "Prior distributions for variance parameters in hierarchical
models" in Bayesian Analysis, 3, 515-533. I am only interested, however,
in recreating the portion of the graph for the overlain prior density
for the half-Cauchy with scale 25 and not the posterior distribution.
However, when I try:
curve(dcauchy, from=0, to=200, location=0, scale=25)
This won't pass location and scale to dcauchy.
You need something along the lines
dcauchy0_25 <- function(x) dcauchy(x, location=0, scale=25)
curve(dcauchy0_25, from=0, to=200)
Uwe Ligges
the probabilities for the half-Cauchy values seem to approach zero
almost immediately after 0 whereas in Gelman 2006 the tail appears much
fatter giving non-zero probabilities out to 100.
I am interested in replicating this because I want to use half-Cauchy
priors and want to play around with the scale values but I want to know
what my prior looks like before using it in models.
Please cc me as I am digest subscriber.
Thanks!
Chris
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