plot.MCMC: Plot posterior summary

In bayeslm: Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors

Plot posterior summary

Description

plot.MCMC is an S3 method to plot empirical distribution of posterior draws. The input is a MCMC matrix

Usage

## S3 method for class 'MCMC'
plot(x,names,burnin=trunc(.1*nrow(X)),tvalues,TRACEPLOT=TRUE,DEN=TRUE,INT=TRUE,
      CHECK_NDRAWS=TRUE,... )

Arguments

x	A MCMC class matrix of posterior draws, such as bayeslm\$beta.
names	an optional character vector of names for the columns of X.
burnin	Number of draws to burn-in (default value is 0.1*nrow(X)).
tvalues	vector of true values.
TRACEPLOT	logical, TRUE provide sequence plots of draws and acfs (default: TRUE)
DEN	logical, TRUE use density scale on histograms (default: TRUE)
INT	logical, TRUE put various intervals and points on graph (default: TRUE)
CHECK_NDRAWS	logical, TRUE check that there are at least 100 draws (default: TRUE)
...	optional arguments for generic function.

Details

This function is modified from package bayesm by Peter Rossi. It plots summary of posterior draws.

Author(s)

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

See Also

summary.bayeslm.fit

Examples

x = matrix(rnorm(1000), 100, 10)
y = x %*% rnorm(10) + rnorm(100)
fit=bayeslm(y~x)
plot(fit$beta)

bayeslm documentation built on June 28, 2022, 1:05 a.m.