The CBDA.pipeline() function comprises all the input specifications to run a set M of subsamples from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized. This version 1.0.0 is fully tested ONLY on continuous features Xtemp and binary outcome Ytemp.
CBDA.pipeline(job_id, Ytemp, Xtemp, label = "CBDA_package_test",
alpha = 0.2, Kcol_min = 5, Kcol_max = 15, Nrow_min = 30,
Nrow_max = 50, misValperc = 0, M = 3000, N_cores = 1, top = 1000,
workspace_directory = setwd(tempdir()), max_covs = 100, min_covs = 5,
algorithm_list = c("SL.glm", "SL.xgboost", "SL.glmnet", "SL.svm",
"SL.randomForest", "SL.bartMachine"))This is the ID for the job generator in the LONI pipeline interface
This is the output variable (vector) in the original Big Data
This is the input variable (matrix) in the original Big Data
This is the label appended to RData workspaces generated within the CBDA calls
Percentage of the Big Data to hold off for Validation
Lower bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)
Upper bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)
Lower bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)
Upper bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)
Percentage of missing values to introduce in BigData (used just for testing, to mimic real cases).
Number of the BigData subsets on which perform Knockoff Filtering and SuperLearner feature mining
Number of Cores to use in the parallel implementation (default is set to 1 core)
Top predictions to select out of the M (must be < M, optimal ~0.1*M)
Directory where the results and workspaces are saved (set by default to tempdir())
Top features to display and include in the Validation Step where nested models are tested
Minimum number of top features to include in the initial model for the Validation Step (it must be greater than 2)
List of algorithms/wrappers used by the SuperLearner. By default is set to the following list algorithm_list <- c("SL.glm","SL.xgboost", "SL.glmnet","SL.svm","SL.randomForest","SL.bartMachine")
CBDA object with validation results and 3 RData workspaces