help midas mhalso see: midas

Title

midas mh -- Bayesian estimation via Metropolis-Hastings MCMC

Syntax

midas mh
tp fp fn tn
[if exp]
[in range]
,
id(varlist)
covariance(structure)
[chains(#)
mcsize(#)
burn(#)
thin(#)
seed(#)
dots(#)
parallel
convergestats
muprior(string)
sigmaprior(string)
phiprior(string)
rhoprior(string)
lamdaprior(string)
hpd
level(#)
noheader
nocoefficients
nosummary
nofitstat
hetstats
hsroc
revman]

Description

midas mh fits the bivariate random-effects model using a
Metropolis-Hastings MCMC sampler with user-specifiable prior distributions.
Seven covariance prior structures are available, ranging from the standard
inverse-Wishart to more flexible hierarchical priors.

Covariance structures

iwishart inverse-Wishart (default)

cholesky Cholesky decomposition

spherical spherical parameterisation

cholefisher Cholesky with Fisher z correlation

product product-normal

sciwishart scaled inverse-Wishart

hiwishart hierarchical inverse-Wishart

Key options

chains(#) number of MCMC chains. Default 4.

mcsize(#) post-burn-in iterations per chain. Default 20000.

burn(#) burn-in iterations. Default 20000.

thin(#) thinning interval. Default 1.

seed(#) random seed. Default 12345.

dots(#) display a dot every # iterations. Default 10000.

parallel runs chains in parallel using Stata's parallel package.

convergestats displays Gelman-Rubin R-hat and effective sample size.

hpd reports highest posterior density (HPD) intervals instead of equal-tail intervals.

muprior(string) prior for the mean logit parameters.
Default normal(0,100).

sigmaprior(string) prior for standard deviations.
Default cauchy(0,2.5).

Example

. midas mh tp fp fn tn, id(author) covariance(cholesky) chains(4) mcsize(10000) hpd

. midas bayesplot

Also see

midas, midas hmc, midas inla, midas bayesplot