Hi,
what I see so far is that you have specified your user.prior.density correctly.
The error comes from the prior precision matrix "B0" in combination with the
marginal.likelihood set to "Laplace". B0, if not explicitly specified, defaults
to zero, which results in eigenvalues of zero. If "Laplace" is indicated for
the marginal.likelihood, the algorithm usually calls an optimization over
"logpost.logit" in "BayesianFactors.R" where the matrix B0 is tried to be
solved by solve(B0) ... as it is a zero matrix its linear equation system is
exactly singular and cannot be solved. The Function MCMClogit knows about this
fact and gives out a warning "Cannot calculate marginal likelihood with
improper prior" while changing marginal.likelihood to "none".
So concluding: Choose your user.prior.density with marginal.likelihood = "none"
and all is fine (implicitly it is done so nevertheless).
Best
Simon
P.S. Using a name on a community help list will certainly improve the number of
answers to your questions.
On Jul 29, 2013, at 3:00 AM, ba0728 <haleyb...@att.net> wrote:
> I'm an undergrad who is new to MCMCpack and I haven't been able to find an
> answer to my problem online yet: I'm attempting to run MCMClogit with a
> Cauchy proper prior but I'm getting the warning "Cannot calculate marginal
> likelihood with improper prior" (my purposes require the marginal likelihood
> calculation so I understand that I need to use a proper prior).
>
> I'm trying to simulate the "user-defined independent Cauchy prior with
> additional args" as specified in the MCMCpack User Manual (p. 76, April 2013
> version). My input data has been standardized (mean = 0, sd = 0.5 for
> non-binary variables, and binary variables with mean of 0 and difference of
> 1 between upper and lower ends) according to the Gelman 2008 paper on
> logistic regression
> (www.stat.columbia.edu/~gelman/research/published/priors11.pdf).
>
> When I run the example data set (birthwt) from the User Manual, the
> logpriorfun works correctly allowing the marginal likelihood to be
> generated. However, when I try running my data with the logprior fun, I get
> a warning that the prior is improper. Here is the code I am running:
>
> *logpriorfun = function(beta, location,scale){
> sum(dcauchy(beta, location, scale, log = TRUE))
> }*
>
> *> MCMC.2= MCMClogit(DEAD ~ YEARS + MALE + x1 + x2 + x3+ x4 +x5 + x6 + x7 +
> x8 + x9, tune= 0.65,burnin =500, mcmc=5000, data = dat, marginal.likelihood
> = "Laplace", user.prior.density=logpriorfun, logfun=TRUE, location = 0,
> scale=2.5)
> *
>
> *@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
> The Metropolis acceptance rate was 0.27418
> @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
> Warning message:
> In MCMClogit(DEAD ~ YEARS + MALE + x1 + x2 + x3 + :
> Cannot calculate marginal likelihood with improper prior*
>
> Any advice on how to fix my arguments so it is a proper prior and will allow
> me to generate a marginal likelihood using the Laplace approximation? Or how
> should I be coding a Cauchy proper prior? I'm having problems defining the
> priors.
>
> Thanks, B.
>
>
>
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/MCMClogit-Cannot-calculate-marginal-likelihood-with-improper-prior-tp4672561.html
> Sent from the R help mailing list archive at Nabble.com.
>
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