Displaying 1 result from an estimated 1 matches for "train_sample".
2012 Jan 17
1
Scoring using cox model: probability of survival before time t
...####################################
library(survival)
n = 100
beta1 = 3; beta2 = -2;
lambdaT = .01
lambdaC = .6
x1 = rnorm(n,0)
x2 = rnorm(n,0)
T = rweibull(n, shape=1, scale=lambdaT*exp(-beta1*x1-beta2*x2))
C = rweibull(n, shape=1, scale=lambdaC)
time = pmin(T,C)
event = time==T
train_sample=data.frame(time,event,x1,x2)
rm(time,event,x1,x2)
fit_coxph <- coxph(Surv(time, event)~ x1 + x2, data= train_sample,
method="breslow")
#Save model to some directory
save(fit_coxph, file = file.path("C:/Desktop","fit_coxph.RData"))
#I can get probabilities on t...