CBDA.pipeline {CBDA}R Documentation

Training/Leaning Step for Compressive Big Data Analytics - LONI PIPELINE

Description

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.

Usage

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"))

Arguments

job_id

This is the ID for the job generator in the LONI pipeline interface

Ytemp

This is the output variable (vector) in the original Big Data

Xtemp

This is the input variable (matrix) in the original Big Data

label

This is the label appended to RData workspaces generated within the CBDA calls

alpha

Percentage of the Big Data to hold off for Validation

Kcol_min

Lower bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)

Kcol_max

Upper bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)

Nrow_min

Lower bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)

Nrow_max

Upper bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)

misValperc

Percentage of missing values to introduce in BigData (used just for testing, to mimic real cases).

M

Number of the BigData subsets on which perform Knockoff Filtering and SuperLearner feature mining

N_cores

Number of Cores to use in the parallel implementation (default is set to 1 core)

top

Top predictions to select out of the M (must be < M, optimal ~0.1*M)

workspace_directory

Directory where the results and workspaces are saved (set by default to tempdir())

max_covs

Top features to display and include in the Validation Step where nested models are tested

min_covs

Minimum number of top features to include in the initial model for the Validation Step (it must be greater than 2)

algorithm_list

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")

Value

CBDA object with validation results and 3 RData workspaces


[Package CBDA version 1.0.0 Index]