Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google

Simple



> s.survreg <- survreg(s.Surv ~ age, data=simple)
> summary(s.survreg)



Call:
survreg(formula = s.Surv ~ age, data = simple)
             Value Std. Error     z        p
(Intercept) 10.928     1.2622  8.66 4.81e-18
age         -0.145     0.0201 -7.22 5.35e-13
Log(scale)  -1.979     0.4541 -4.36 1.31e-05

Scale= 0.138 

Weibull distribution
Loglik(model)= -5.9   Loglik(intercept only)= -12.7
	Chisq= 13.64 on 1 degrees of freedom, p= 0.00022 
Number of Newton-Raphson Iterations: 11 
n= 10



> s.pred <- predict(s.survreg, simple)
> s.pred.q <- predict(s.survreg, simple, type="quantile")
> result <- cbind(data.frame(simple$time, s.pred), s.pred.q)
> names(result) <- c("Actual", "Predicted", "Lower", "Upper")
> head(result)



  Actual  Predicted      Lower      Upper
1      1  38.797001  28.424796  43.538082
2      2   2.118099   1.551835   2.376935
3      3  80.262215  58.804471  90.070438
4      5 343.508197 251.672817 385.485671
5      7   6.777773   4.965765   7.606032
6     11  38.797001  28.424796  43.538082



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