CBDA_Stopping_Criteria.pipeline: Stopping Criteria function for Compressive Big Data Analytics

In CBDA: Compressive Big Data Analytics

Description

This CBDA function generates a stopping criteria for the *max_covs - min_covs* nested predictive models generated in the previous step. It also populates the CBDA object.

Usage

CBDA_Stopping_Criteria.pipeline(label = "CBDA_package_test", Kcol_min = 5,
  Kcol_max = 15, Nrow_min = 30, Nrow_max = 50, misValperc = 0,
  M = 3000, workspace_directory = tempdir(), max_covs = 100,
  min_covs = 5, lambda = 1.005)

Arguments

label	This is the label appended to RData workspaces generated within the CBDA calls
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
workspace_directory	Directory where the results and workspaces are saved
max_covs	Top features to 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
lambda	Fisher test threshold for MSE (=1.005 by default)

Value

value

CBDA documentation built on May 1, 2019, 8:23 p.m.