make_ITR_famTEMsel: make individualized treatment recommendations (ITRs) based on...

In syhyunpark/famTEMsel: Functional Additive Models for Treatment Effect-Modifier Selection

Description

The function make_ITR_famTEMsel returns individualized treatment decision recommendations for subjects with pretreatment characteristics newX and newZ, given a famTEMsel object, famTEMsel.obj, and an (optimal) regularization parameter index, lambda.index.

Usage

make_ITR_famTEMsel(famTEMsel.obj, newX = NULL, newZ = NULL,
  lambda.index = NULL, maximize = TRUE)

Arguments

famTEMsel.obj	a famTEMsel object, containing the fitted constrained functional additive models.
newX	a length-p list of new values for the functional-valued covariates X, where the jth element is a (n-by-n.eval[j]) matrix of the observed jth function, at which predictions are to be made; if NULL, X from the training set is used.
newZ	a (n-by-q) matrix of new values for the scalar-valued covariates Z at which predictions are to be made; if NULL, Z from the training set is used.
lambda.index	an index of the regularization parameter lambda at which predictions are to be made; one can supply lambda.opt.index obtained from the function cv.famTEMsel(); the default is NULL, in which case the predictions are made at the lambda.index used in obtaining famTEMsel.obj.
maximize	default is TRUE, assuming a larger value of the outcome is better; if FALSE, a smaller value is assumed to be prefered.

Value

pred.new	a (n-by-L) matrix of predicted values, with each column representing one of the L treatment options.
trt.rule	a (n-by-1) vector of the individualized treatment recommendations

Author(s)

Park, Petkova, Tarpey, Ogden

See Also

famTEMsel,cv.famTEMsel, predict_famTEMsel

syhyunpark/famTEMsel documentation built on Feb. 6, 2020, 12:13 a.m.