Description Usage Arguments Value
View source: R/CBDA_Stopping_Criteria.pipeline.R
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.
1 2 3 4 | 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)
|
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
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