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

In BoomSpikeSlab: MCMC for Spike and Slab Regression

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 class '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.

Details

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

Author(s)

Steven L. Scott

See Also

PlotMarginalInclusionProbabilities PlotLmSpikeCoefficients PlotLmSpikeResiduals PlotModelSize lm.spike SpikeSlabPrior summary.lm.spike predict.lm.spike

Examples

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")

BoomSpikeSlab documentation built on May 29, 2024, 5:07 a.m.