tune_params_ml: Tuning parameter selection
In pablogonzalezginestet/stackBagg: Stacked IPCW Bagging

tune_params_ml

R Documentation

Tuning parameter selection

Tuning 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.