predict.fcrr {fastcmprsk} | R Documentation |
Predicts cumulative incidence function from a fcrr
object.
## S3 method for class 'fcrr' predict(object, newdata, getBootstrapVariance = TRUE, var.control = varianceControl(B = 100, useMultipleCores = FALSE), type = "none", alpha = 0.05, tL = NULL, tU = NULL, ...)
object |
Output from |
newdata |
A set of covariate values to predict the CIF. |
getBootstrapVariance |
Logical: Calculate variance for CIF via bootstrap. |
var.control |
List of variance parameters from |
type |
Confidence intervals or confidence bands. |
alpha |
Significance level to compute intervals or bands |
tL |
Lower time for band estimation. |
tU |
Upper time for band estimation. |
... |
additional arguments affecting the fastCrr procedure. |
B |
Number of bootstrap samples for variance estimation. |
Calculates the CIF using fcrr
output conditional on newdata
.
Returns a list of class predict.fcrr
.
ftime |
Unique observed failure times |
CIF |
predicted CIF at time |
lower |
lower interval/band limit |
upper |
upper interval/band limit |
type |
same as original argument |
Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.
library(fastcmprsk) set.seed(10) ftime <- rexp(200) fstatus <- sample(0:2, 200, replace = TRUE) cov <- matrix(runif(1000), nrow = 200) dimnames(cov)[[2]] <- c('x1','x2','x3','x4','x5') fit <- fastCrr(Crisk(ftime, fstatus) ~ cov, returnDataFrame = TRUE) cov2 <- rnorm(5) predict(fit, newdata = cov2)