summary.lm.spike: Numerical summaries of the results from a spike and slab...

In BoomSpikeSlab: MCMC for Spike and Slab Regression

Numerical summaries of the results from a spike and slab regression.

Description

Produces a summary of the marginal distribution of model coefficients from a spike and slab regression.

Usage

## S3 method for class 'lm.spike'
summary(object, burn = 0, order = TRUE, ...)

Arguments

object	An object of class lm.spike.
burn	The number of MCMC iterations in the ojbect to be discarded as burn-in.
order	Logical. If TRUE then the coefficients are presented in order of their posterior inclusion probabilities. Otherwise the order of the coefficients is the same as in object.
...	Unused. Present for compatibility with generic summary().

Value

Returns a list with the following elements:

coefficients	A five-column matrix with rows representing model coefficients. The first two columns are the posterior mean and standard deviation of each coefficient, including the point mass at zero. The next two columns are the posterior mean and standard deviations conditional on the coefficient being nonzero. The last column is the probability of a nonzero coefficient.
residual.sd	A summary of the posterior distribution of the residual standard deviation parameter.
rsquare	A summary of the posterior distribution of the R^2 statistic: 1 - residual.sd^2 / var(y)

Author(s)

Steven L. Scott

See Also

lm.spike SpikeSlabPrior plot.lm.spike predict.lm.spike

Examples

  n <- 100
  p <- 10
  ngood <- 3
  niter <- 1000
  sigma <- 2

  x <- cbind(1, matrix(rnorm(n * (p-1)), nrow=n))
  beta <- c(rnorm(ngood), rep(0, p - ngood))
  y <- rnorm(n, x %*% beta, sigma)
  x <- x[,-1]
  model <- lm.spike(y ~ x, niter=niter)
  plot(model)
  plot.ts(model$beta)
  hist(model$sigma)  ## should be near 8
  summary(model)

BoomSpikeSlab documentation built on May 28, 2022, 1:11 a.m.