gen_config: Generate Config File Skeleton
In NSAPH/airpred: A Framework For Building Air Pollution Estimation Models

Description Usage Arguments Details Value

Generate Config File Skeleton

1	gen_config(default = TRUE, path = ".", in_list = NULL)

`default`	A boolean determining whether or not default values should be loaded into the generated config file
`path`	the directory that the config file should be saved in
`in_list`	An optional list that can be passed in to generate values for a config file. Currently, the list is not validated, so the config file is not guaranteed to work with the structure.

The following are the items contained in the config file. All of them must be present in order for the model to run successfully.

impute A boolean. If TRUE, airpred will generate imputations for specified variables
impute_vars Either "default" or a path to a yaml file listing the variables to be imputed. If a custom file is being used, the top level of the yaml file should be "base" with the elements of "base" being the names of the variables being imputed.
impute_formula Either "default" or a path to a yaml file listing the variables to be used as inputs to the imputation model. If a custom file is being used, the top level of the yaml file should be "base" or the name of a variable being imputed with the elements being the names of the variables being used for imputation.
standardize A boolean. If TRUE, airpred will perform Z score standardization on specified variables
finalday The date of the last day covered by the data set
csv_path The path where the assembled data is stored as a csv
rds_path The path where the assembled data is stored as an rds file
date_var The name of the variable containing date identification
site_var the name of the variable containing site identification
output_var The name of the variable that the model is trying to predict
imputation_models The path where the imputation models should be saved.
mid_process_data The path where data should be saved between imputation, normalization and transformation steps
training_models A list of the models to be used in training and used for the ensemble model.
two_stage Should the two stage modeling process be implemented?
monitor_list The location of the file containing the coordinates of the monitors
training_data The file containing transformed and imputed code to be used for training. Currently must be an RDS file.
training_output The directory to be used for storing the output of the training models
predict_data The input data for a given round of prediction
predict_mid_process The directory that holds all saved files generated in the prediction process.
predict_output The directory that holds the generated predictions
pre_generated_weights A boolean determining whether or not the spatial weights are stored on disk or need to be generated on the fly from the list of monitors
weight_matrix_path The path to where pregenerated weights are stored