View source: R/summary.sl.time.R
summary.sl.time | R Documentation |
Return goodness-of-fit indicators of a Super Learner obtained by the function sl.time
.
## S3 method for class 'sl.time'
summary(object, ..., method, pro.time, newdata, times, failures)
object |
An object returned by the function |
... |
Additional arguments affecting the plot. |
method |
A character string with the name of the algorithm included in the SL for wich the calibration plot is performed. The defaut is "sl" for the Super Learner. |
pro.time |
The prognostic time up to which the time-dependent indicators are estimated. |
newdata |
An optional data frame containing the new sample for validation with covariate values, folow-up times, and event status. The default value is |
times |
The name of the variable related the numeric vector with the follow-up times in |
failures |
The name of the variable related the numeric vector with the event indicators (0=right censored, 1=event) in |
The following metrics are returned: "brier" 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 Log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time
.
Yohann Foucher <Yohann.Foucher@univ-poitiers.fr>
Camille Sabathe <camille.sabathe@univ-nantes.fr>
data(dataDIVAT2)
dataDIVAT2$train <- 1*rbinom(n=dim(dataDIVAT2)[1], size = 1, prob=1/2)
# The training of the super learner with 2 algorithms from the
# first 100 patients of the training sample
sl<-sl.time(method=c("aft.gamma", "ph.gompertz"), metric="ibs",
data=dataDIVAT2[dataDIVAT2$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(sl)
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