summary.bayesm.mat: Summarize Mcmc Parameter Draws

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

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 class '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

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).

Author(s)

Peter Rossi, Anderson School, UCLA, [e-mail].

See Also

summary.bayesm.var, summary.bayesm.nmix

Examples

## Not run: out=rmnpGibbs(Data,Prior,Mcmc); summary(out$betadraw)

bayesm documentation built on Sept. 24, 2023, 1:07 a.m.