# summary.lm.spike: Numerical summaries of the results from a spike and slab... In BoomSpikeSlab: MCMC for Spike and Slab Regression

## Description

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

## Usage

 ```1 2``` ```## 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

`lm.spike` `SpikeSlabPrior` `plot.lm.spike` `predict.lm.spike`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ``` 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) ```