plot.BayesFBHborrow {BayesFBHborrow}

Plot the MCMC results

Description

S3 object which produces different plots depending on the "type" variable

Usage

## S3 method for class 'BayesFBHborrow'
plot(x, x_lim, estimator = NULL, type = NULL, ...)

Arguments

x

object of class "BayesFBHborrow" to be visualized

x_lim

x-axis to be used for plot

estimator

which estimate to be visualized

type

The type of plot to be produced, "trace" will produce a trace plot of the "fixed" parameters, "hist" will give a histogram for the "fixed" parameters, and "matrix" will plot the mean and quantiles of a given sample.

...

other plotting arguments, see plot_trace(), plot_hist(), plot_matrix() for more information

Value

ggplot2 object

Examples

data(weibull_cc, package = "BayesFBHborrow")

# Set your tuning parameters
tuning_parameters <- list("Jmax" = 5,
                          "pi_b" = 0.5,
                          "cprop_beta" = 0.5)
                          
# run the MCMC sampler
out <- BayesFBHborrow(weibull_cc, NULL, tuning_parameters, 
                      initial_values = NULL,
                      iter = 10, warmup_iter = 1)

# Now let's create a variety of plots

# Staring with a histogram of beta_1 (treatment effect)
gg_hist <- plot(out, NULL, estimator = "beta_1", type = "hist",
                title = "Example histogram of beta_1")

# And an accompanied trace plot of the same parameter                 
gg_trace <- plot(out, 1:10, estimator = "beta_1", type = "trace",
                  title = "Example trace plot", xlab = "iterations",
                  ylab = "beta_1 (treatment effect)")
                  
# Lastly. visualize the smoothed baseline hazard
time_grid <- seq(0, max(weibull_cc$tte), length.out = 2000)
gg_matrix <- plot(out, time_grid, estimator = "out_slam", type = "matrix",
                  title = "Example plot of smoothed baseline hazard",
                  xlab = "time", ylab = "baseline hazard")