GaPloth {BGPhazard}  R Documentation 
Plots for the Hazard and Survival Function Estimates
Description
Plots the hazard function and with the survival function estimates defined by the Markov gamma process with and without covariates (NietoBarajas & Walker, 2002).
Usage
GaPloth(
M,
type.h = "segment",
addSurvival = T,
intervals = T,
confidence = 0.95,
summary = FALSE
)
Arguments
M 
tibble. Contains the output by 
type.h 
character. "segment"= use segments to plot hazard rates, "line" = link hazard rates by a line 
addSurvival 
Logical. If 
intervals 
logical. If TRUE, plots confidence bands for the selected functions including NelsonAalen and/or KaplanMeier estimate. 
confidence 
Numeric. Confidence level. 
summary 
Logical. If 
Details
This function returns estimators plots for the resulting hazard rate as it is computed by GaMRes and CGaMRes and the NelsonAalen estimate along with their confidence intervals for the data set given. Additionally, it plots the survival function and the KaplanMeier estimate with their corresponding credible/confidence intervals.
Value
SUM.h 
Numeric tibble. Summary for the mean, median, and a

SUM.S 
Numeric tibble. Summary for
the mean, median, and a 
References
 NietoBarajas, L. E. (2003). Discrete time Markov gamma processes and time dependent covariates in survival analysis. Bulletin of the International Statistical Institute 54th Session. Berlin. (CDROM).
 NietoBarajas, L. E. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413424.
See Also
GaMRes, CGaMRes, CGaPlotDiag, GaPlotDiag
Examples
## Simulations may be time intensive. Be patient.
## Example 1
# data(gehan)
# timesG < gehan$time[gehan$treat == "6MP"]
# deltaG < gehan$cens[gehan$treat == "6MP"]
# GEX1 < GaMRes(timesG, deltaG, K = 8, iterations = 3000)
# GaPloth(GEX1)
## Example 2
# data(leukemiaFZ)
# timesFZ < leukemiaFZ$time
# deltaFZ < leukemiaFZ$delta
# GEX2 < GaMRes(timesFZ, deltaFZ, type.c = 4)
# GaPloth(GEX2)