Dear friends,
In R there is a file | save to file option which takes a picture of
the text in your screen and saves it to a text file. Is there any way to do
the same with a function? I need this because I run a long program and when
I get the results and try to use file | save to file, the
Dear friends,
I run the Gelman-Rubin Convergence test for a MCMC object I have and I
got the following result Multivariate psrf 1.07+0i, What does this mean? I
guess (if I am not mistaken) that I should get a psrf close to 1.00 but what
is 1.07+0i? Is that convergence or something else?
Jorge
questions about Bayesian analysis and,
particularly stochastic cost frontier analysis. In addition, I offer to
share the little I know about it with anybody interested in it.
Jorge
-Original Message-
From: Icabalceta, Jorge L.
Sent: Thursday, February 05, 2004 1:53 PM
To: '[EMAIL
Dear Friends,
According to Gelman et al (2003), ...Bayesian P-values are defined as
the probability that the replicated data could be more extreme than the
observed data, as measured by the test quantity p=pr[T(y_rep,tetha) =
T(y,tetha)|y]... where p=Bayesian P-value, T=test statistics,
Dear Friends,
According to Gelman et al (2003), ...Bayesian P-values are defined as
the probability that the replicated data could be more extreme than the
observed data, as measured by the test quantity p=pr[T(y_rep,tetha) =
T(y,tetha)|y]... where p=Bayesian P-value, T=test statistics,
I have been running a Gibbs Sampler to estimate levels of efficiency in the
Louisiana Shrimp Industry. I created a matrix (samp) where I stored the
results of each iteration for 86 variables. I run 10,000 iterations. So, the
matrix samp is 10,000 x 86. I want to use the gelman-rubin test to check
Dear Friends,
I am trying to use the gelman-rubin convergence test. I generated a matrix
samp[10,000x86] with the gibbs sampler. the test requires the creation of
mcmc objects. Since I don't know how to define samp as a mcmc object, I
tried to create one mcmc object by means of the mcmc()
Sorry to bother you. I am sort of confused with the random number generation
from a gamma distribution. For some reason, if a variable X~gamma(shape,
scale)I have been told that the mean mu can be eithe mu=shape/scale or
mu=shape*scale. Why is that so? This is making me have doubt about the
random
I was trying to generate random numbers with a gamma distribution. In R the
function is:
rgamma(n, shape, rate = 1, scale = 1/rate). My question is that if
X~gamma(alpha, beta) and I want to generate one random number where do I
plug alpha and beta in rgamma? and, what is the meaning and use of
