ExtractPIPs: Extract posterior inclusion probabilities (PIPs) from BKMR...

In bkmr: Bayesian Kernel Machine Regression

Extract posterior inclusion probabilities (PIPs) from BKMR model fit

Description

Extract posterior inclusion probabilities (PIPs) from Bayesian Kernel Machine Regression (BKMR) model fit

Usage

ExtractPIPs(fit, sel = NULL, z.names = NULL)

Arguments

fit	An object containing the results returned by a the kmbayes function
sel	logical expression indicating samples to keep; defaults to keeping the second half of all samples
z.names	optional argument providing the names of the variables included in the h function.

Details

For guided examples, go to https://jenfb.github.io/bkmr/overview.html

Value

a data frame with the variable-specific PIPs for BKMR fit with component-wise variable selection, and with the group-specific and conditional (within-group) PIPs for BKMR fit with hierarchical variable selection.

Examples

## First generate dataset
set.seed(111)
dat <- SimData(n = 50, M = 4)
y <- dat$y
Z <- dat$Z
X <- dat$X

## Fit model with component-wise variable selection
## Using only 100 iterations to make example run quickly
## Typically should use a large number of iterations for inference
set.seed(111)
fitkm <- kmbayes(y = y, Z = Z, X = X, iter = 100, verbose = FALSE, varsel = TRUE)

ExtractPIPs(fitkm)

bkmr documentation built on March 28, 2022, 9:11 a.m.