CBDA_Stopping_Criteria: Stopping Criteria function for 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(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).

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