tune_params_ml.RdTuning parameter selection
tune_params_ml(gam_param, lasso_param, randomforest_param, knn_param, svm_param, nn_param, bart_param, folds, xnam, tao, data, weighting)
| gam_param | a vector containing degree of freedom 3 and 4 |
|---|---|
| lasso_param | a grid of values for the shrinkage term |
| randomforest_param | a two column matrix: first column denotes the num_trees parameter and the second column denotes the mtry parameter. |
| knn_param | a grid of positive integers values |
| svm_param | a three column matrix: first column denotes the cost parameter, second column the gamma and third column the kernel. kernel=1 denotes "radial" and kernel=2 denotes "linear". |
| nn_param | a grid of positive integers values for the neurons |
| bart_param | a three column matrix: first column denotes the num_tree parameter, second column the k parameter and third column the q parameter. |
| folds | number of folds |
| xnam | vector with the names of the covariates to be included in the model |
| tao | evaluation time point of interest |
| data | data set that contains at least id, E , ttilde, delta, wts and covariates |
| weighting | Procedure to compute the inverse probability of censoring weights. Weighting="CoxPH" and weighting="CoxBoost" model the censoring by the Cox model and CoxBoost model respectively. |
a list with the tune parameters selected using the IPCW AUC loss function.