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]