pip {fkbma}R Documentation

Compute Posterior Inclusion Probabilities (PIPs) for rjMCMC Results

Description

This function returns the posterior inclusion probabilities (PIPs) for all variables, including the intercept and exposure, based on the results from an rjMCMC model.

Usage

pip(results)

Arguments

results

An object of class rjMCMC containing the output from the rjMCMC procedure, which includes:

fixed_param

Matrix of posterior samples for exposure intercept and main effect.

binary_param

Matrix of posterior samples for binary variable parameters.

sigma_sq

Matrix of posterior samples for the residual variance (sigma squared).

vars_prop_summ

Posterior inclusion probabilities for candidate variables.

splines_fitted

List of matrices containing fitted values for spline terms across iterations.

data_fit

Original dataset used in the rjMCMC procedure.

candsplineinter

Names of continuous candidate predictive spline variables.

candsplinevars

Names of continuous candidate spline variables.

candbinaryvars

Names of binary candidate variables.

candinter

Names of interaction terms, which can include spline variables.

mcmc_specs

MCMC sampler specifications, including the number of iterations, burn-in, thinning, and chains.

Value

A numeric vector with the PIPs for the intercept, exposure, and other variables.

Examples


# Example dataset
data("simulated_data")

candsplinevars <- c("X_1")
candbinaryvars <- paste0("Z_", 1:5)
candinter <- c(candsplinevars, candbinaryvars)

results <- rjMCMC(simulated_data, candsplinevars, candbinaryvars, candinter,
                  outcome = "Y", factor_var = "trt")
pip(results)


[Package fkbma version 0.2.0 Index]