plot.lm.spike: Plot the results of a spike and slab regression.

Description

The default plot is a barplot of the marginal inclusion probabilities for each variable, as obtained by PlotMarginalInclusionProbabilities. Other interesting plots can be obtained by supplying a string as the second argument.

Usage

# S3 method for lm.spike
plot(x,
     y = c("inclusion", "coefficients", "scaled.coefficients",
              "residuals", "fit", "size", "help"),
     burn = SuggestBurnLogLikelihood(x$log.likelihood),
     ...)

Arguments

x: An object of class lm.spike.
y: The type of plot desired.
burn: The number of MCMC iterations to discard as burn-in.
...: Additional arguments passed to the specific functions that do the plotting.

Author

Steven L. Scott

Details

The actual plotting will be handled by PlotMarginalInclusionProbabilities, PlotLmSpikeCoefficients, PlotLmSpikeResiduals, or PlotModelSize. See the appropriate function for more options.

Examples

Run this code

simulate.lm.spike <- function(n = 100, p = 10, ngood = 3, niter=1000, sigma = 8){
  x <- cbind(matrix(rnorm(n * (p-1)), nrow=n))
  beta <- c(rnorm(ngood), rep(0, p - ngood))
  y <- rnorm(n, beta[1] + x %*% beta[-1], sigma)
  draws <- lm.spike(y ~ x, niter=niter)
  return(invisible(draws))
}
model <- simulate.lm.spike(n = 1000, p = 50, sigma = .3)
plot(model, inclusion.threshold = .01)

plot(model, "size")