variance_probs-internal {sMSROC}

Variance of the predictive model

Description

Estimation of the variance of the predictive model by bootstrap.

Usage

variance_probs(marker, outcome, status, observed.time, left, right, time,
               meth, data_type, grid, probs, ci.nboots, parallel, ncpus, all)

Arguments

marker

vector with the biomarker values.

outcome

vector with the condition of the subjects as positive, negative or unknown at the considered time time.

status

response vector with the outcome values. The highest one is assumed to stand for the subjects having the event under study.

observed.time

vector with the observed times for each subject.

left

vector with the lower edges of the observed intervals.

right

vector with the upper edges of the observed intervals.

time

point of time at which the sMS ROC curve estimator will be computed.

meth

method for approximating the predictive model P(D|X=x).

data_type

scenario handled.

grid

grid size.

probs

vector containing the probabilities estimated through to the predictive model.

ci.nboots

number of bootstrap samples.

parallel

indicates whether parallel computing will be done or not.

ncpus

number of CPUs to use if parallel computing is performed.

all

indicates whether the probabilities from the predictive model should be considered or not.

Value

List with a single component:

sd.probs

vector containing the standard deviation of the probabilities of the predictive model.