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Process and Save Forecast Output to Files

Source: R/process_casts.R
process-forecast-output.Rd

Take the model fit and forecast output, process them into savable objects, and save them to the output folders.
The forecast metadata file is updated accordingly to track the saved output.
add_observations_to_forecast_table appends a column of observations to a forecast's forecast tab. If a model interpolated a data set, it adds the true (non-interpolated) observations so that model predictions are all compared to the same data.
select_forecasts determines the forecasts that match user specifications. Functionally, it is a wrapper on read_forecasts_metadata with filtering for specifications that provides a simple user interface to the large set of available forecasts via the metadata.

Usage

process_model_output(
  main = ".",
  model_fit = NULL,
  model_forecast,
  model,
  dataset,
  species
)

read_forecast_table(main = ".", forecast_id = NULL)

read_forecasts_tables(main = ".", forecasts_ids = NULL)

add_observations_to_forecast_table(main = ".", forecast_table = NULL)

read_forecast_metadata(main = ".", forecast_id = NULL)

read_model_fit(main = ".", forecast_id = NULL)

read_model_forecast(main = ".", forecast_id = NULL)

select_forecasts(
  main = ".",
  forecasts_metadata = NULL,
  forecasts_ids = NULL,
  forecasts_groups = NULL,
  models = NULL,
  datasets = NULL,
  species = NULL,
  historic_end_newmoonnumbers = NULL
)

read_forecasts_metadata(main = ".")

Arguments

main

character value of the name of the main component of the directory tree.

model_fit, model_forecast

Output from a model's fit and forecast functions.

model, models

character values of the name(s) of the model(s) of interest, as indexed within the directory in the forecasts sub folder. See the forecasts metadata file (forecasts_metadata.csv) for summary information. If NULL (the default), the most recently generated forecast's output is read in.
models can be NULL, one value, or more than one values, model can only be NULL or one value.

dataset, datasets

character vector of the rodent dataset name(s) to include. datasets can be NULL, one value, or more than one values, dataset can only be NULL or one value.

species

character value of the species codes (or "total" for the total across species) to include. Default value is NULL, which equates to no selection with respect to species.

forecast_id, forecasts_ids

integer (or integer numeric) value(s) representing the forecast(s) of interest, as indexed within the directory in the casts sub folder. See the forecasts metadata file (forecasts_metadata.csv) for summary information. If NULL (the default), the most recently generated forecast's output is read in.
forecasts_ids can be NULL, one value, or more than one values, forecast_id can only be NULL or one value.

forecast_table

A data.frame of a cast's output. See read_forecast_table.

forecasts_metadata

data.frame of forecast metadata. If NULL (default), will try to read via read_forecasts_metadata.

forecasts_groups

integer (or integer numeric) value of the forecast groups to include. Default value is NULL, which equates to no selection with respect to forecast_group.

historic_end_newmoonnumbers

integer (or integer numeric) newmoon numbers of the forecast origin. Default value is NULL, which equates to no selection.

Value

process_model_output: relevant elements are saved to external files, and returned as a list.
read_forecast_table: forecast table data.frame.
read_forecast_tables: data.frame of combined forecast tables.
add_observations_to_forecast_table: forecast table data.frame with an observation column added.
read_forecast_metadata: list of forecast_metadata.
read_model_fit: forecast output (typically as a list).
read_model_forecast: forecast output (typically as a list).
select_forecasts: data.frame of selected forecasts' metadata.
read_forecasts_metadata: data.frame of forecasts' metadata.

Details

Four model-specific output components are saved and returned: * forecast_metadata: saved out with write_yaml. * forecast_tab: saved using write_csv_arrow. * model_fit: saved out as a serialized JSON file via serializeJSON and read_json, so quite flexible with respect to specific object structure. * model_forecast: saved out as a serialized JSON file via serializeJSON and read_json, so quite flexible with respect to specific object structure.

See also

Core forecasting functions: ensemble, evaluate forecasts, portalcast()

Examples

if (FALSE) { # \dontrun{
   main1 <- file.path(tempdir(), "forecast_output")

   setup_dir(main = main1)
   dataset <- "all"
   species <- "DM"
   model   <- "AutoArima"
 
   abundance      <- prepare_abundance(main    = main1,
                                       dataset = dataset,
                                       species = species,
                                       model   = model)
   model_controls <- models_controls(main      = main1,
                                     models    = model)[[model]]
   metadata       <- read_metadata(main        = main1)
   newmoons       <- read_newmoons(main        = main1)                                        
   covariates     <- read_covariates(main      = main1)
 
   fit_args  <- named_null_list(element_names = names(model_controls$fit$args))
   for (i in 1:length(fit_args)) {
     fit_args[[i]] <- eval(parse(text = model_controls$fit$args[i]))
   }
   model_fit  <- do.call(what = model_controls$fit$fun,
                         args = fit_args)
 
 
   forecast_args  <- named_null_list(element_names = names(model_controls$forecast$args))
   for (i in 1:length(forecast_args)) {
     forecast_args[[i]] <- eval(parse(text = model_controls$forecast$args[i]))
   }
 
   model_forecast <- do.call(what = model_controls$forecast$fun,
                             args = forecast_args)
 
   process_model_output(main           = main1,
                        model_fit      = model_fit,
                        model_forecast = model_forecast,
                        model          = model,
                        dataset        = dataset,
                        species        = species) 

   cast_table     <- read_forecast_table(main = main1)
   cast_table2    <- add_observations_to_forecast_table(main = main1,
                                                        forecast_table = cast_table)
   cast_tables    <- read_forecast_tables(main = main1)
   cast_metadata  <- read_forecast_metadata(main = main1)
   cast_forecast  <- read_model_forecast(main = main1)

   casts_metadata <- read_forecasts_metadata(main = main1)
   select_forecasts(main = main1)

   unlink(main1, recursive = TRUE)
} # }