CGaPloth {BGPhazard}

Plots for the Hazard and Survival Funcion Estimates for the Bayesian non-parametric Markov gamma model with covariates in survival analysis.

Description

Plots the resulting hazard function along with the survival function estimate defined by the Markov gamma process with covariates (Nieto-Barajas, 2003).

Usage

CGaPloth(
  M,
  new_obs = NULL,
  type.h = "segment",
  coxSurv = T,
  intervals = T,
  confidence = 0.95,
  summary = FALSE
)

Arguments

M

tibble. Contains the output generated by CuMRres.

new_obs

tibble. The function calculates the hazard rates and survival function estimates for specific individuals expressed in a tibble, the names of the columns have to be the same as the data input.

type.h

character. "segment"= use segments to plot hazard rates, "line" = link hazard rates by a line

coxSurv

logical. Add estimated Survival function with the Cox-Model

intervals

logical. If TRUE, plots confidence bands for the selected functions including Cox-Model.

confidence

Numeric. Confidence level.

summary

logical. If TRUE, a summary for hazard and survival functions is returned as a tibble.

Details

This function return plots for the resulting hazard rate as it is computed by CGaMRes and the quantile of Tao specified by the user aswell as an annotation. In the same plot the credible intervals for both variables are plotted; The mean of Pi is also annotated. Additionally, it plots the survival function with their corresponding credible intervals.

Value

SUM.h

Numeric tibble. Summary for the mean, median, and a confint / 100 confidence interval for each segment of the hazard function. If summary = TRUE

SUM.S

Numeric tibble. Summary for the mean, median, and a confint / 100 confidence interval for each segment of the survival function. If summary = TRUE

References

- Nieto-Barajas, L. E. (2003). Discrete time Markov gamma processes and time dependent covariates in survival analysis. Bulletin of the International Statistical Institute 54th Session. Berlin. (CD-ROM).

- Nieto-Barajas, L. E. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413-424.

See Also

CGaMRes,

Examples

## Simulations may be time intensive. Be patient.

   # ## Example 1
   #  data(leukemiaFZ)
   #  leukemia1 <- leukemiaFZ
   #  leukemia1$wbc <- log(leukemiaFZ$wbc)
   #  CGEX1 <- CGaMRes(data = leukemia1, K = 10, iterations = 100, thinning = 1)
   #  CGaPloth(CGEX1)