MCMCprobit {MCMCpack} | R Documentation |
This function generates a posterior density sample from a probit regression model using the data augmentation approach of Albert and Chib (1993). The user supplies data and priors, and a sample from the posterior density is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.
MCMCprobit(formula, data = list(), burnin = 1000, mcmc = 10000, thin = 5, verbose = FALSE, seed = 0, beta.start = NA, b0 = 0, B0 = 0, bayes.resid = FALSE, ...)
formula |
Model formula. |
data |
Data frame. |
burnin |
The number of burn-in iterations for the sampler. |
mcmc |
The number of Gibbs iterations for the sampler. |
thin |
The thinning interval used in the simulation. The number of Gibbs iterations must be divisible by this value. |
verbose |
A switch which determines whether or not the progress of the sampler is printed to the screen. If TRUE, the iteration number and the betas are printed to the screen. |
seed |
The seed for the random number generator. The code uses the Mersenne Twister, which requires an integer as an input. If nothing is provided, the Scythe default seed is used. |
beta.start |
The starting value for the beta vector. This can either be a scalar or a column vector with dimension equal to the number of betas. If this takes a scalar value, then that value will serve as the starting value for all of the betas. The default value of NA will use the maximum likelihood estimate of beta as the starting value. |
b0 |
The prior mean of beta. This can either be a scalar or a column vector with dimension equal to the number of betas. If this takes a scalar value, then that value will serve as the prior mean for all of the betas. |
B0 |
The prior precision of beta. This can either be a scalar or a square matrix with dimensions equal to the number of betas. If this takes a scalar value, then that value times an identity matrix serves as the prior precision of beta. Default value of 0 is equivalent to an improper uniform prior on beta. |
bayes.resid |
Should latent Bayesian residuals (Albert and Chib, 1995) be returned? Default is FALSE meaning no residuals should be returned. Alternatively, the user can specify an array of integers giving the observation numbers for which latent residuals should be calculated and returned. TRUE will return draws of latent residuals for all observations. |
... |
further arguments to be passed |
MCMCprobit
simulates from the posterior density of a probit
regression model using data augmentation. The simulation
proper is done in compiled C++ code to maximize efficiency. Please consult
the coda documentation for a comprehensive list of functions that can be
used to analyze the posterior density sample.
The model takes the following form:
y_i ~ Bernoulli(pi_i)
Where the inverse link function:
pi_i = Phi(x_i'beta)
We assume a multivariate Normal prior on beta:
beta ~ N(b0,B0^(-1))
See Albert and Chib (1993) for estimation details.
An mcmc object that contains the posterior density sample. This object can be summarized by functions provided by the coda package.
Albert, J. H. and S. Chib. 1993. ``Bayesian Analysis of Binary and Polychotomous Response Data.'' J. Amer. Statist. Assoc. 88, 669-679
Albert, J. H. and S. Chib. 1995. ``Bayesian Residual Analysis for Binary Response Regression Models.'' Biometrika. 82, 747-759.
Andrew D. Martin, Kevin M. Quinn, and Daniel Pemstein. 2003. Scythe Statistical Library 0.4. http://scythe.wustl.edu.
Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. 2002. Output Analysis and Diagnostics for MCMC (CODA). http://www-fis.iarc.fr/coda/.
## Not run: data(birthwt) posterior <- MCMCprobit(low~age+as.factor(race)+smoke, data=birthwt) plot(posterior) summary(posterior) ## End(Not run)