summary.rjMCMC {fkbma} | R Documentation |
Summarize Results from Reversible Jump MCMC (rjMCMC)
Description
This function provides a detailed summary of the results from the rjMCMC
procedure, including
model information, parameter estimates, posterior inclusion probabilities, convergence diagnostics,
and plots for spline terms. The function also prints the model formula with fbs()
notation for spline terms,
indicating the use of free-knot B-splines.
Usage
## S3 method for class 'rjMCMC'
summary(object, digits = 3, level = 0.95, pip_cutoff = 0.1, ...)
Arguments
object |
An object of class rjMCMC containing the output from the
|
digits |
Number of digits in summary output (default = 3) |
level |
Credible interval level (default = 0.95) |
pip_cutoff |
Posterior inclusion probability cutoff for reporting effective sample size and R-squared (default = 0.10) |
... |
Additional arguments to be passed to other methods or functions. |
Details
The function produces detailed summaries similar to those from brms
, including
diagnostics, estimates, posterior inclusion probabilities, and spline effects. The spline terms
are wrapped in fbs()
notation, indicating the use of free-knot B-splines in the model. If the sampler
did not converge, a warning is issued. The function also allows the user to view diagnostic plots for fitted
exposure effects.
Value
Prints the following summary information:
- Model Formula
The model formula with spline terms wrapped in
fbs()
, indicating free-knot B-splines, and interaction terms appropriately formatted.- Convergence Diagnostics
Reports any convergence issues based on Geweke diagnostics.
- MCMC Sampler Arguments
Displays MCMC sampler arguments, including the number of posterior samples, burn-in, thinning, and chains.
- Parameter Estimates
Posterior mean, standard error, 95% credible intervals, effective sample size (ESS), Gelman-Rubin statistic (Rhat), and posterior inclusion probabilities (PIP) for binary parameters, exposure intercept, and exposure effect.
- Gaussian Family Parameters
Posterior summary for the residual standard error (sigma).
- Posterior Inclusion Probabilities for Splines
Prints the posterior inclusion probabilities for spline terms.
- Plots for Fitted Exposure Effects
Plots the mean and 95% credible intervals for each spline term vs fitted exposure effects.
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")
summary(results)