Design hold-out model — model_design

Use selected model design settings to create a model design for hold-out data. The hold-out data can be out-of-sample data or subsetted data for k-fold cross validation.

Usage

model_design_outsample(
  project,
  mod.name,
  outsample.mod.name = NULL,
  CV = FALSE,
  CV_dat = NULL,
  use.scalers = FALSE,
  scaler.func = NULL
)

Arguments

project: Name of project
mod.name: Name of saved model to use. Argument can be the name of the model or can pull the name of the saved "best" model. Leave mod.name empty to use the saved "best" model. If more than one model is saved, mod.name should be the numeric indicator of which model to use. Use table_view("modelChosen", project) to view a table of saved models.
outsample.mod.name: Name assigned to out-of-sample model design. Must be unique and not already exist in model design list. If outsample.mod.name = NULL then a default name will be chosen based on mod.name, which is the default value.
CV: Logical, Indicates whether the model design is being created for cross validation TRUE, or for simple out- of-sample dataset. Defaults to CV = TRUE.
CV_dat: Training or testing dataset for k-fold cross validation.
use.scalers: Input for create_model_input(). Logical, should data be normalized? Defaults to FALSE. Rescaling factors are the mean of the numeric vector unless specified with scaler.func.
scaler.func: Input for create_model_input(). Function to calculate rescaling factors.

Details

This function automatically pulls model settings from the selected model and creates an alternative choice matrix, expected catch/revenue matrices, and model design for a hold-out dataset. The hold-out data set can be an out-of-sample dataset or subset of main data for cross validation. If running out-of-sample data, this function requires that a filtered out-of-sample data file (.rds file) exists in the output folder. For cross validation, this function is called in the cross_validation() function. Note: the out-of-sample functions only work with a single selected model at a time. To run out-of-sample functions on a new out-of-sample dataset, start with load_outsample() if an entirely new dataset or filter_outsample().

Examples

if (FALSE) {

# For out-of-sample dataset
model_design_outsample("scallop", "scallopModName")

}