CGaPloth {BGPhazard}  R Documentation 
Plots for the Hazard and Survival Funcion Estimates for the Bayesian nonparametric 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 (NietoBarajas, 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 
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 CoxModel 
intervals 
logical. If TRUE, plots confidence bands for the selected functions including CoxModel. 
confidence 
Numeric. Confidence level. 
summary 
logical. If 
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

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
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)