BePloth {BGPhazard}

Plots for the discrete Hazard and Survival Function Estimates

Description

Plots the resulting hazard function along with the survival function estimates defined by the Markov beta process (Nieto-Barajas and Walker, 2002).

Usage

BePloth(
  M,
  type.h = "dot",
  add.survival = T,
  intervals = T,
  confidence = 0.95,
  summary = FALSE
)

Arguments

M

tibble. Contains the output generated by BeMRres.

type.h

character, "line" = plots the hazard rate of each interval joined by a line, "dot" = plots the hazard rate of each interval with a dot.

add.survival

logical, If TRUE, plots the Nelson-Alen based estimate in the same graphic of the hazard rate and the Kaplan-Meier estimates of the survival function.

intervals

logical. If TRUE, plots confidence bands for the selected functions including Nelson-Aalen and/or Kaplan-Meier estimate.

confidence

Numeric. Confidence band width.

summary

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

Details

This function returns estimators plots for the hazard rate as computed by BeMRes together with the Nelson-Aalen estimate along with their confidence intervals for the data set given. Additionally, it plots the survival function and the Kaplan-Meier estimate with their corresponding credible intervals.

Value

SUM.h

Numeric tibble. Summary for the mean, median, and a confint / 100 confidence interval for each failure time of the hazard function.

SUM.S

Numeric tibble. Summary for the mean, median, and a confint / 100 confidence interval for each failure time of the survival function.

References

- 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

BeMRes, BePlotDiag

Examples

## Simulations may be time intensive. Be patient.

## Example 1
#  data(psych)
#  timesP <- psych$time
#  deltaP <- psych$death
#  BEX1 <- BeMRes(timesP, deltaP, iterations = 3000, burn.in = 300, thinning = 1)
#  BePloth(BEX1)
#  sum <- BePloth(BEX1, type.h = "line", summary = T)

## Example 2
#  data(gehan)
#  timesG <- gehan$time[gehan$treat == "control"]
#  deltaG <- gehan$cens[gehan$treat == "control"]
#  BEX2 <- BeMRes(timesG, deltaG, type.c = 2, c.r = rep(50, 22))
#  BePloth(BEX2)