Extract posterior inclusion probabilities (PIPs) from BKMR model fit

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.

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.

Details

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

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)
#> Iteration: 10 (10% completed; 0.00505 secs elapsed)
#> Iteration: 20 (20% completed; 0.01022 secs elapsed)
#> Iteration: 30 (30% completed; 0.01576 secs elapsed)
#> Iteration: 40 (40% completed; 0.02146 secs elapsed)
#> Iteration: 50 (50% completed; 0.02673 secs elapsed)
#> Iteration: 60 (60% completed; 0.03172 secs elapsed)
#> Iteration: 70 (70% completed; 0.03673 secs elapsed)
#> Iteration: 80 (80% completed; 0.04177 secs elapsed)
#> Iteration: 90 (90% completed; 0.04729 secs elapsed)
#> Iteration: 100 (100% completed; 0.05301 secs elapsed)

ExtractPIPs(fitkm)
#>   variable  PIP
#> 1       z1 1.00
#> 2       z2 1.00
#> 3       z3 0.00
#> 4       z4 0.28