Visualize Portalcasting Data and Forecasts

plot_forecasts_error_lead: lots the raw error (estimate - observation) as a function of lead time across model runs from different forecast origins for multiple models and multiple species (or total) within a data set.
plot_covariates: plots an observed timeseries and forecast timeseries of the covariates used.
plot_forecast_ts: plots an observed timeseries and forecast timeseries with a prediction interval. Observations that occurred after the forecast are shown connected directly to the pre-cast observation data (as the black solid line with points).
plot_forecast_point: plots the point value with confidence interval for a time point across multiple species. Casts can be selected either by supplying a forecast_id number or any combination of dataset, model, and historic_end_newmoonnumber, which filter the available forecasts in unison. This plot type can only handle output from a single forecast, so if multiple forecasts still remain, the one with the highest number is selected. To be more certain about forecast selection, use the forecast_id input.
plot_forecasts_cov_RMSE: plots the coverage (fraction of predictions within the CI) and RMSE (root mean squared error) of each model among multiple species.

Usage

plot_forecasts_error_lead(
  main = ".",
  forecasts_ids = NULL,
  forecasts_evaluations = NULL,
  historic_end_newmoonnumbers = NULL,
  models = NULL,
  datasets = NULL,
  species = NULL
)

plot_forecasts_cov_RMSE(
  main = ".",
  forecasts_metadata = NULL,
  forecasts_ids = NULL,
  forecasts_evaluations = NULL,
  historic_end_newmoonnumbers = NULL,
  models = NULL,
  datasets = NULL,
  species = NULL
)

plot_forecast_point(
  main = ".",
  forecasts_metadata = NULL,
  forecast_id = NULL,
  dataset = NULL,
  model = NULL,
  historic_end_newmoonnumber = NULL,
  species = NULL,
  highlight_sp = NULL,
  newmoonnumber = NULL,
  with_census = FALSE
)

plot_forecast_ts(
  main = ".",
  forecasts_metadata = NULL,
  forecast_id = NULL,
  dataset = NULL,
  model = NULL,
  historic_start_newmoonnumber = NULL,
  historic_end_newmoonnumber = NULL,
  species = NULL
)

plot_covariates(main = ".", to_plot = "ndvi")

Arguments

main: character value of the name of the main component of the directory tree.
forecasts_evaluations: data.frame of forecast evaluations, as returned from evaluate_forecasts. If NULL (default), will try to read via read_forecasts_evaluations.
species: character vector of the species code(s) or "total" for the total across species) to be plotted NULL translates to the species defined by forecasting_species called by prefab_species.
forecasts_metadata: data.frame of forecast metadata. If NULL (default), will try to read via read_forecasts_metadata.
forecast_id, forecasts_ids: integer (or integer numeric) values representing the forecasts of interest for restricting plotting, as indexed within the directory in the casts sub folder. See the forecasts metadata file (forecasts_metadata.csv) for summary information. forecast_id can only be length-1 or NULL, whereas forecasts_ids is not length-restricted.
dataset, datasets: character value of the rodent data set(s) to include. dataset can only be length-1 or NULL, whereas datasets is not length-restricted.
model, models: character value(s) of the name of the model to include. Default value is NULL, which equates to no selection with respect to model or models. model can only be length-1 or NULL, whereas models is not length-restricted.
historic_end_newmoonnumber, historic_end_newmoonnumbers: integer (or integer numeric) newmoon number(s) of the forecast origin. Default value is NULL, which equates to no selection. historic_end_newmoonnumber can only be length-1 or NULL, whereas historic_end_newmoonnumbers is not length-restricted.
highlight_sp: character vector of the species codes (or "total" for the total across species) to be highlighted or NULL (default) to not highlight anything.
newmoonnumber: integer (or integer numeric) newmoon number for the plot.
with_census: logical toggle if the plot should include the observed data collected during the predicted census.
historic_start_newmoonnumber: integer (or integer numeric) newmoon number for the beginning of the x-axis of the plot.
Does not influence the fit of the models, just the presentation.
to_plot: character of the covariate to plot, restricted to column names in the covariates table (see read_covariates).

Value

NULL. Plot is generated.

Details

Casts can be selected either by supplying a forecast_id number or any combination of dataset, model, and historic_end_newmoonnumber, which filter the available forecasts in unison. This plot type can only handle output from a single forecast, so if multiple forecasts still remain, the one with the highest number is selected. To be more certain about forecast selection, use the forecast_id input.
As of portalcasting v0.9.0, the line and bands in plot_forecast_ts and point and bars in plot_forecast_point represent the mean and the 95 percent prediction interval.

Examples

if (FALSE) { # \dontrun{
   main1 <- file.path(tempdir(), "figures")
   setup_production(main = main1)

   plot_covariates(main = main1)

   portalcast(main = main1, models = "AutoArima")

   ids <- select_forecasts(main     = main3, 
                           species  = c("DM", "PP", "total"),
                           models   = c("AutoArima", "ESSS", "pevGARCH", "nbGARCH", "jags_RW"),
                           datasets = c("all", "controls"))$forecast_id
   nids         <- length(ids)
   nsample_ids  <- 1000
   forecasts_ids <- ids[round(seq(1, nids, length.out = nsample_ids))]
   evaluate_forecasts(main         = main3, 
                      forecasts_ids = forecasts_ids) 

   plot_forecast_ts(main = main1)
   plot_forecast_point(main = main1)
   plot_forecasts_error_lead(main = main1)
   plot_forecasts_cov_RMSE(main    = main1, 
                           models  = "AutoArima", 
                           species = "DM")

   unlink(main1, recursive = TRUE)
} # }