plots: Visualize Portalcasting Data and Forecasts

In weecology/portalPredictionsModels: Model and Forecast Portal Rodent Dynamics

Visualize Portalcasting Data and Forecasts

Description

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).

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.

Value

NULL. Plot is generated.

Examples

## Not run: 
   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)

## End(Not run)

weecology/portalPredictionsModels documentation built on Jan. 31, 2024, 12:03 p.m.