BGPhazard (version 2.1.1)

CuPloth: Plots for the Hazard and Survival Funcion Estimates

Description

Plots the hazard function and the survival function estimates defined by the bayesian semiparametric cure rate model with an unknown threshold (Nieto-Barajas & Yin, 2008).

Usage

CuPloth(
  M,
  type.h = "segment",
  intervals = T,
  confidence = 0.95,
  qn = 0.5,
  summary = FALSE,
  position_label = "right"
)

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 a grid of the survival function. If summary = TRUE

Arguments

M

tibble. Contains the output generated by CuMRres.

type.h

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

intervals

logical. If TRUE, plots credible intervals.

confidence

Numeric. Confidence level.

qn

Numeric. Quantile for Tao that should be visualized on the plot.

summary

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

position_label

character. Labels on the right or left side of the plot.

Details

This function return estimators plots for the resulting hazard rate as it is computed by CuMRes and the cure time (quantile of Tao specified by the user), together with credible intervals. Additionally, it plots the survival function and the cure proportion estimates with their corresponding credible intervals.

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., & 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

See Also

CuMRes,

Examples

## Simulations may be time intensive. Be patient.

## Example 1
# data(crm3)
    # times<-crm3$times
    # delta<-crm3$delta
    # res <- CuMRes(times, delta, type.t = 2, length = .1,
    #                   K = 100, alpha = rep(1, 100  ),
    #                   beta = rep(1, 100),c.r = rep(50, 99),
    #                   iterations = 100, burn.in = 10, thinning = 1, type.c = 2)
    # CuPloth(res, type.h = "segment",qn=.5, summary = T)
    # CuPloth(res, type.h = "line",qn=.5)