summary.bayesm.mat: Summarize Mcmc Parameter Draws

Description

summary.bayesm.mat is an S3 method to summarize marginal distributions given an array of draws

Usage

# S3 method for bayesm.mat
summary(object, names, burnin = trunc(0.1 * nrow(X)), 
  tvalues, QUANTILES = TRUE, TRAILER = TRUE,...)

Arguments

object: object (hereafter X) is an array of draws, usually an object of class bayesm.mat
names: optional character vector of names for the columns of X
burnin: number of draws to burn-in (def: \(0.1*nrow(X)\))
tvalues: optional vector of "true" values for use in simulation examples
QUANTILES: logical for should quantiles be displayed (def: TRUE)
TRAILER: logical for should a trailer be displayed (def: TRUE)
...: optional arguments for generic function

Author

Peter Rossi, Anderson School, UCLA, perossichi@gmail.com.

Details

Typically, summary.bayesm.nmix will be invoked by a call to the generic summary function as in summary(object) where object is of class bayesm.mat. Mean, Std Dev, Numerical Standard error (of estimate of posterior mean), relative numerical efficiency (see numEff), and effective sample size are displayed. If QUANTILES=TRUE, quantiles of marginal distirbutions in the columns of \(X\) are displayed.

summary.bayesm.mat is also exported for direct use as a standard function, as in summary.bayesm.mat(matrix).

summary.bayesm.mat(matrix) returns (invisibly) the array of the various summary statistics for further use. To assess this array usestats=summary(Drawmat).

Examples

Run this code

if (FALSE) out=rmnpGibbs(Data,Prior,Mcmc); summary(out$betadraw)