Estimation of the variance of the predictive model by bootstrap.
variance_probs(marker, outcome, status, observed.time, left, right, time,
meth, data_type, grid, probs, ci.nboots, parallel, ncpus, all)List with a single component:
vector containing the standard deviation of the probabilities of the predictive model.
vector with the biomarker values.
vector with the condition of the subjects as positive, negative or unknown at the considered time time.
response vector with the outcome values. The highest one is assumed to stand for the subjects having the event under study.
vector with the observed times for each subject.
vector with the lower edges of the observed intervals.
vector with the upper edges of the observed intervals.
point of time at which the sMS ROC curve estimator will be computed.
method for approximating the predictive model \(P(D|X=x)\).
scenario handled.
grid size.
vector containing the probabilities estimated through to the predictive model.
number of bootstrap samples.
indicates whether parallel computing will be done or not.
number of CPUs to use if parallel computing is performed.
indicates whether the probabilities from the predictive model should be considered or not.