plot.bayesm.mat {bayesm} | R Documentation |
plot.bayesm.mat
is an S3 method to plot arrays of MCMC draws. The columns in the
array correspond to parameters and the rows to MCMC draws.
## S3 method for class 'bayesm.mat': plot(x,names,burnin,tvalues,TRACEPLOT,DEN,INT,CHECK_NDRAWS, ...)
x |
An object of either S3 class, bayesm.mat, or S3 class, mcmc |
names |
optional character vector of names for coefficients |
burnin |
number of draws to discard for burn-in, def: .1*nrow(X) |
tvalues |
vector of true values |
TRACEPLOT |
logical, TRUE provide sequence plots of draws and acfs, def: TRUE |
DEN |
logical, TRUE use density scale on histograms, def: TRUE |
INT |
logical, TRUE put various intervals and points on graph, def: TRUE |
CHECK_NDRAWS |
logical, TRUE check that there are at least 100 draws, def: TRUE |
... |
standard graphics parameters |
Typically, plot.bayesm.mat
will be invoked by a call to the generic plot function as in
plot(object)
where object is of class bayesm.mat. All of the bayesm
MCMC routines
return draws in this class (see example below). One can also simply invoke
plot.bayesm.mat
on any valid 2-dim array as in plot.bayesm.mat(betadraws)
.
plot.bayesm.mat
paints (by default) on the histogram:
green "[]" delimiting 95% Bayesian Credibility Interval
yellow "()" showing +/- 2 numerical standard errors
red "|" showing posterior mean
plot.bayesm.mat
is also exported for use as a standard function, as in
plot.bayesm.mat(matrix)
Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.
## ## not run # out=runiregGibbs(Data,Prior,Mcmc) # plot(out$betadraw) #