Wrap function for computing the sMS estimator in diagnosis scenarios.
sMS_binout(marker, status, meth, grid, probs, all)The returned value is a list with the following components:
vector with the weighted empirical estimator of the sensitivity.
vector with the weighted empirical estimator of the specificity.
vector containing the points between 0 and 1 at which the ROC curve estimator will be computed. Its size is determined by the grid parameter.
ROC curve approximated at each point of the vector u.
area under the weighted empirical ROC curve estimator.
vector with the ordered biomarker values.
vector with the probabilities of the predictive model corresponding to each biomarker value.
vector with the biomarker values.
numeric response vector.
method for approximating the predictive model \(P(D|X=x)\).
``E'', allocates to each individual their own condition as positive or negative. Those whose condition is unknown at time time are dismissed.
``L'', for Linear logistic regression models (see details in sMSROC).
``S'', for Smooth models (see details sMSROC).
grid size.
vector with the probabilities from the predictive model when it is manually entered.
parameter indicating whether all probabilities given by the predictive model should be considered (value ``T'') or just those corresponding to individuals whose condition as positive or negative is unknown (``F''). The default value is (``T'').
The function obtains the probabilities corresponding to the predictive model (first stage of the sMS ROC curve estimator). If they were not manually entered, the functions pred.mod.emp or pred.mod.binout are called depending on the chosen meth. Then, it calls the function computeROC to compute the weighted empirical ROC curve estimator (second stage).
pred_mod_emp, pred_mod_binout,
computeROC, sMS_timerc and sMS_timeic