cvWtTuning {ClinicalUtilityRecal} | R Documentation |
Calibration weights require specification of tuning parameter delta or lambda. This function uses K-fold cross-validation to select tuning parameter used for calibration weights, with standardized net benfeit (sNB) as objective function. Either one of delta or lambda must be specificed. The sequence of tuning parameters can be obtained from the RAWgrid
function.
cvWtTuning(p,y,r,rl,ru,kFold=5,cvParm,tuneSeq,cv.seed=1111)
y |
Vector of binary outcomes, with 1 indicating event (cases) and 0 indicating no event (controls) |
p |
Vector of risk score values |
r |
Clinically relevant risk threshold |
rl |
Lower bound of clinically relevant region |
ru |
Upper bound of clinically relevant region |
kFold |
Number of folds for cross-validation |
cvParm |
Parameter to be selected via cross-validation. Can be either delta the weight assigned to observations outside the clinically relevant region [R_l,R_u], or the lambda tuning parameter controlling exponential decay within the clinically relevant region [R_l,R_u] |
tuneSeq |
Sequence of values of tuning parameters to perform cross-validation over |
cv.seed |
Intial seed set for random splitting of data into K folds |
cv.res |
Matrix containing sequence of tuning parameters and corresponding cross-validation sNB |
cv.param |
Value of tuning parameter selected via cross validation |
cv.full |
Matrix of cross-validation results for all folds |
Note this function does not split data into training and validaion set, but performs the K-fold cross-validation procedure on all data included. We advise that a separate, validation subset should be split from the data used in this function.
Anu Mishra
Mishra, A. (2019). Methods for Risk Markers that Incorporate Clinical Utility (Doctoral dissertation). (Available Upon Request)
calWt
,
RAWgrid
,
nb
,
cvRepWtTuning