CBDA_Validation.pipeline: CBDA Validation function for Compressive Big Data Analytics -...

In CBDA: Compressive Big Data Analytics

Description

This CBDA function generates *max_covs - min_covs* nested models based on the ranking returned by the *Consolidation* function. It also consolidates all the *max_covs - min_covs* workspaces into a single one.

Usage

CBDA_Validation.pipeline(job_id_val, 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 = tempdir(), max_covs = 100,
  min_covs = 5)

Arguments

job_id_val	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
top	Top predictions to select out of the M
workspace_directory	Directory where the results and workspaces are saved
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

Value

value

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