CCuPlotDiag: Diagnostics plots for Lambda, Theta, Delta, U, C, Pi, Z and Epsilon. Hazard function, cure proportion and cure time for the median observation.

Description

Diagnostic plots for hazard rate (Lambda), regression parameters for the hazard (Theta), regression parameters for the cure rate (Delta), latent variable (U), dependence parameter (C), mean of cure threshold (Mu), cure proportion (Pi), cure threshold (Z) and the parameter of the hierarchical prior (Epsilon).

Usage

CCuPlotDiag(M, variable = "Lambda", pos = 1)

Arguments

M: tibble. Contains the output by CCuMRes.
variable: Either "Lambda", "U", "C", "Mu", "Pi", "Z" or "Epsilon". Variable for which diagnostic plot will be shown.
pos: Positive integer. Position of the selected variable to be plotted.

Details

This function returns a diagnosyics plot for which the chain for the selected variable can be monitored. Diagnostics includes trace, ergodic mean, autocorrelation function and histogram.

References

Nieto-Barajas, L. E., & Yin, G. (2008). Bayesian semiparametric cure rate model with an unknown threshold. Scandinavian Journal of Statistics, 35(3), 540-556. https://doi.org/10.1111/j.1467-9469.2007.00589.x

Examples

Run this code




## Simulations may be time intensive. Be patient.

## Example 1
# data(BMTKleinbook)
# res <- CCuMRes(BMTKleinbook, covs.x = c("tTransplant","hodgkin","karnofsky","waiting"),
#                covs.y = c("tTransplant","hodgkin","karnofsky","waiting"),
#                        type.t = 2, K = 72, length = 30,
#                        alpha = rep(2,72), beta = rep(2,72), c.r = rep(50, 71), type.c = 2,
#                        var.delta.str = .1, var.theta.str = 1,
#                        var.delta.ini = 100, var.theta.ini = 100,
#                        iterations = 100, burn.in = 10, thinning = 1)
# CCuPlotDiag(M = res, variable = "Z")
# CCuPlotDiag(M = res, variable = "Pi.m")
# CCuPlotDiag(M = res, variable = "Lambda", pos = 2)
# CCuPlotDiag(M = res, variable = "U", pos = 4)