summary.sltime {survivalSL} | R Documentation |
Summaries of a Super Learner
Description
Return goodness-of-fit indicators of a Super Learner obtained by the function survivalSL
.
Usage
## S3 method for class 'sltime'
summary(object, method="sl", newdata=NULL,
ROC.precision=seq(.01,.99,.01), digits=7, ...)
Arguments
object |
An object returned by the function |
method |
A character string with the name of the algorithm included in the SL for which the calibration plot is performed. The default is "sl" for the Super Learner. |
newdata |
An optional data frame containing the new sample for validation with covariate values, follow-up times, and event status. The default value is |
ROC.precision |
An optional argument with the percentiles (between 0 and 1) of the prognostic variable used for computing each point of the time dependent ROC curve. 0 (min) and 1 (max) are not allowed. By default, the precision is |
digits |
An optional integer for the number of digits to print when printing numeric values. |
... |
Additional arguments affecting the summary which are passed from |
Details
The following metrics are returned: "ci" for the concordance index at the prognostic time pro.time
, "bs" for the Brier score at the prognostic time pro.time
, "ibs" for the integrated Brier score up to the last observed time of event, "ibll" for the integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "ribs" for the restricted Integrated Brier score up to the prognostic time pro.time
, "ribll" for the restricted integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial log-likelihood, and "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time
.
Value
No return value for this S3 method.
See Also
Examples
data(dataDIVAT2)
dataDIVAT2$train <- 1*rbinom(n=dim(dataDIVAT2)[1], size = 1, prob=1/2)
# The training of the super learner with 2 algorithms from the
# first 100 patients of the training sample
sl1 <- survivalSL(method=c("LIB_AFTgamma", "LIB_PHgompertz"), metric="auc",
data=dataDIVAT2[dataDIVAT2$train==1,][1:100,], times="times", failures="failures",
pro.time = 12, cov.quanti=c("age"), cov.quali=c("hla", "retransplant", "ecd"),
cv=3)
# The prognostic capacities from the same training sample
summary(sl1)