cal_surv_prob | R Documentation |
This function calculates the individual survival probability from a fitted riAFT-BART model at desired values of times
cal_surv_prob( object, time.points, test.only = FALSE, train.only = FALSE, cluster.id )
object |
A fitted object from riAFTBART_estimate() function. |
time.points |
A numeric vector representing the points at which the survival probability is computed. |
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. |
cluster.id |
A vector of integers representing the cluster id. The cluster id should be an integer and start from 1. |
A list with the following two components
Surv: |
A matrix of survival probabilities for each individual. |
time.points: |
The time point entered. |
library(riAFTBART) set.seed(20181223) n = 50 # 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 = 50, M.keep = 50, 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)
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