tune_params_ml: Tuning parameter selection

In pablogonzalezginestet/ensBagg: Stacked IPCW Bagging

Tuning parameter selection

Description

Tuning parameter selection

Usage

tune_params_ml(gam_param, lasso_param, randomforest_param, knn_param,
  svm_param, nn_param, bart_param, folds, xnam, tao, data, weighting)

Arguments

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.

Value

a list with the tune parameters selected using the IPCW AUC loss function.

pablogonzalezginestet/ensBagg documentation built on Aug. 25, 2023, 3:23 a.m.