Description Usage Arguments Value
View source: R/CBDA_Validation.pipeline.R
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.
1 2 3 4 5 | 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)
|
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
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