How to calculate survival probabilities in R? -
i trying fit parametric survival model. think managed so. however, not succeed in calculating survival probabilities:
library(survival) zaman <- c(65,156,100,134,16,108,121,4,39,143,56,26,22,1,1,5,65, 56,65,17,7,16,22,3,4,2,3,8,4,3,30,4,43) test <- c(rep(1,17),rep(0,16)) wbc <- c(2.3,0.75,4.3,2.6,6,10.5,10,17,5.4,7,9.4,32,35,100, 100,52,100,4.4,3,4,1.5,9,5.3,10,19,27,28,31,26,21,79,100,100) status <- c(rep(1,33)) data <- data.frame(zaman,test,wbc) surv3 <- surv(zaman[test==1], status[test==1]) fit3 <- survreg( surv3 ~ log(wbc[test==1]),dist="w") on other hand, no problem @ while calculating survival probabilities using kaplan-meier estimation:
fit2 <- survfit(surv(zaman[test==0], status[test==0]) ~ 1) summary(fit2)$surv any idea why?
you can predicted probabilities survreg object predict:
predict(fit3) if you're interested in combining original data, , in residual , standard errors of predictions, can use augment function in broom package:
library(broom) augment(fit3) a full analysis might like:
library(survival) library(broom) data <- data.frame(zaman, test, wbc, status) subdata <- data[data$test == 1, ] fit3 <- survreg( surv(zaman, status) ~ log(wbc), subdata, dist="w") augment(fit3, subdata) with output:
zaman test wbc status .fitted .se.fit .resid 1 65 1 2.30 1 115.46728 43.913188 -50.467281 2 156 1 0.75 1 197.05852 108.389586 -41.058516 3 100 1 4.30 1 85.67236 26.043277 14.327641 4 134 1 2.60 1 108.90836 39.624106 25.091636 5 16 1 6.00 1 73.08498 20.029707 -57.084979 6 108 1 10.50 1 55.96298 13.989099 52.037022 7 121 1 10.00 1 57.28065 14.350609 63.719348 8 4 1 17.00 1 44.47189 11.607368 -40.471888 9 39 1 5.40 1 76.85181 21.708514 -37.851810 10 143 1 7.00 1 67.90395 17.911170 75.096054 11 56 1 9.40 1 58.99643 14.848751 -2.996434 12 26 1 32.00 1 32.88935 10.333303 -6.889346 13 22 1 35.00 1 31.51314 10.219871 -9.513136 14 1 1 100.00 1 19.09922 8.963022 -18.099216 15 1 1 100.00 1 19.09922 8.963022 -18.099216 16 5 1 52.00 1 26.09034 9.763728 -21.090343 17 65 1 100.00 1 19.09922 8.963022 45.900784 in case, .fitted column predicted probabilities.
Comments
Post a Comment