Estimation of the predictive models in diagnosis scenarios.
pred_model_binout(marker, status, meth)The returned value is a list with the two components:
vector containing the ordered marker values.
vector with the probabilities corresponding to each marker value estimated through the predictive model.
vector with the biomarker values.
numeric response vector. The highest value is assumed to stand for the subjects having the event under study. The lowest value, for those who do not. Any other value will not be considered.
method for approximating the predictive model \(P(D|X=x)\). The options are:
``L'', for Linear logistic regression models.
``S'', for Smooth models.
If meth = ``L'', the logit transformation of the predicitive model is approximated by a linear logistic regression model:
$$P (D|X=x) = 1/(1 + \exp{- \{ \beta_0 + \beta_1 x \}),}$$
with \(\beta_0, \beta_1 \in {\cal R}\).
If meth = ``S'', the logit transformation of the predicitive model is estimated by the smooth logistic regression,
$$ P(D | X=x) = 1 / ( 1 + \exp \{ - s(x) \}),$$ being \(s(\cdot)\) the smooth function (splines, tools:::Rd_expr_doi("10.1002/sim.4780080504")).
sMS_binout and sMSROC