Plots for the Hazard and Survival Funcion Estimates for the Bayesian non-parametric Markov gamma model with covariates in survival analysis.
Plots the resulting hazard function along with the survival function estimate defined by the Markov gamma process with covariates (Nieto-Barajas, 2003).
CGaPloth( M, new_obs = NULL, type.h = "segment", coxSurv = T, intervals = T, confidence = 0.95, summary = FALSE )
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 Cox-Model |
intervals |
logical. If TRUE, plots confidence bands for the selected functions including Cox-Model. |
confidence |
Numeric. Confidence level. |
summary |
logical. If |
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.
SUM.h |
Numeric tibble. Summary for the mean, median, and a
|
SUM.S |
Numeric tibble. Summary for
the mean, median, and a |
- 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.
## 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)
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