riAFTBART (version 0.3.2)

plot.riAFTBART_survProb: Plot the fitted survival curves from riAFT-BART model

Description

This function plot the mean/individual survival curves from a fitted riAFT-BART model

Usage

# S3 method for riAFTBART_survProb
plot(x, test.only = FALSE, train.only = TRUE, id = NULL, ...)

Arguments

x

An object from cal_surv_prob() function.

test.only

A logical indicating whether or not only data from the test set should be computed. The default is FALSE.

train.only

A logical indicating whether or not only data from the training set should be computed. The default is FALSE.

id

A vector representing the IDs for the individual survival curves to plot. The default is NULL and the mean survival curves will be plotted.

...

further arguments passed to or from other methods.

Value

A plot

Examples

# NOT RUN {
library(riAFTBART)
set.seed(20181223)
n = 5       # number of clusters
k = 50      # cluster size
N = n*k     # total sample size
cluster.id = rep(1:n, each=k)
tau.error = 0.8
b = stats::rnorm(n, 0, tau.error)
alpha = 2
beta1 = 1
beta2 = -1
sig.error = 0.5
censoring.rate = 0.02
x1 = stats::rnorm(N,0.5,1)
x2 = stats::rnorm(N,1.5,0.5)
trt.train = sample(c(1,2,3), N, prob = c(0.4,0.3,0.2), replace = TRUE)
trt.test = sample(c(1,2,3), N, prob = c(0.3,0.4,0.2), replace = TRUE)
error = stats::rnorm(N,0,sig.error)
logtime = alpha + beta1*x1 + beta2*x2 + b[cluster.id] + error
y = exp(logtime)
C = rexp(N, rate=censoring.rate) # censoring times
Y = pmin(y,C)
status = as.numeric(y<=C)
res <- riAFTBART_fit(M.burnin = 10, M.keep = 10, M.thin = 1, status = status,
                      y.train = Y, trt.train = trt.train, trt.test = trt.test,
                      x.train = cbind(x1,x2),
                      x.test = cbind(x1,x2),
                      cluster.id = cluster.id)
surv_prob_res <- cal_surv_prob(object = res, time.points = sort(exp(logtime)),
test.only = TRUE, cluster.id = cluster.id)
plot(x = surv_prob_res, test.only = TRUE, train.only = FALSE)
# }