BGPhazard (version 1.2.3)

CGaPloth: Plots for the Hazard and Survival Function Estimates

Description

Plots the resulting hazard function along with the survival function estimate defined by the Markov gamma process with covariates (Nieto-Barajas, 2003).

Usage

CGaPloth(M, fun = "both", confint = TRUE, h.NA = FALSE, KM = FALSE, 
confidence = 0.95, summary = FALSE, legend = TRUE)

Arguments

M

List. Contains the information given for lambda and u (generated by CGaMRres).

fun

"h"=plots hazard function; "S"= plots survival function and "both"=plots both the hazard and survival functions.

confint

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

h.NA

Logical. If TRUE, Nelson-Aalen estimate is plotted over the hazard function.

KM

Logical. If TRUE, Kaplan-Meier estimate is plotted over the survival function.

confidence

Numeric. Confidence band width.

summary

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

legend

Logical. If FALSE, legend is not shown.

Value

SUM.h

Numeric matrix. Summary for the mean, median, and a confint / 100 confidence interval for each segment of the hazard function.

SUM.S

Numeric matrix. Summary for the mean, median, and a confint / 100 confidence interval for each segment of the survival function.

Warning

The argument fun is case-sensitive and requires quotations as its class is character.

Details

This function return plots for the resulting hazard rate as it is computed by CGaMRes and 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 confidence 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. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413-424.

See Also

CGaMRes, CGaPlotDiag

Examples

# NOT RUN {
## 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 = 10000, thpar = 10)
#  CGaPloth(CGEX1, fun = "both", confint = TRUE)

## Example 2. Refer to "Cox-gamma model example" section in package vignette for details.
#  SampWeibull <- function(n, a = 10, b = 1, beta = c(1, 1)) {
#    M <- matrix(0, ncol = 7, nrow = n)
#    for(i in 1:n){
#      M[i, 1] <- i
#      M[i, 2] <- x1 <- runif(1)
#      M[i, 3] <- x2 <- runif(1)
#      M[i, 4] <- rweibull(1, shape = b, 
#                          scale = 1 / (a * exp(cbind(x1, x2) %*% beta)))
#      M[i, 5] <- rexp(1)
#      M[i, 6] <- M[i, 4] > M[i, 5]
#      M[i, 7] <- min(M[i, 4], M[i, 5])
#      }
#    colnames(M) <- c("i", "x_i1", "x_i2", "t_i", "c_i", "delta", "min{c_i, d_i}")
#    return(M)
#  }
#  dat <- SampWeibull(100, 0.1, 1, c(1, 1))
#  dat <- cbind(dat[, c(4, 6)], dat[, c(2, 3)])
#  CG <- CGaMRes(dat, K = 10, iterations = 3000, thpar = 10)
#  CGaPloth(CG)
# }