On 28/05/2010 9:29 AM, Christopher David Desjardins wrote:
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)
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.
Don't ignore the warnings!!! The scale argument is not being passed to
dcauchy. (Nothing in the help page suggests it would be, but some other
similar functions would have passed it, so I can see how you made the
wrong assumption. But why did you ignore all those warnings?) You'll
get what you want with
curve( dcauchy(x, location=0, scale=25), from=0, to=200)
or with
den <- function(x) dcauchy(x, location=0, scale=25)
curve(den, from=0, to=200)
if you don't like using the magic name "x" in the first one.
Duncan Murdoch
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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