View source: R/output_accuracy.R
assess.accuracy | R Documentation |
High level handler function to decide which metrics to compute and then call the code to compute them. At present, only continuous and discrete outcomes are supported.
assess.accuracy( predictions.mat, observations.vec, model.vec, locations.vec, year.vec, forecast.target, threshold = "default", percentage = "default" )
predictions.mat |
A matrix where each row corresponds to a separate forecast, and every column corresponds to a forecast realization. In the case of point forecasts, there will be a single column. In the case of probabilistic forecasts, there will likely be a thousand or more columns. Note that these are realizations, and not the probabilities associated with categories. |
observations.vec |
A vector where each entry corresponds to the observed outcome for the corresponding row in predictions.mat. |
model.vec |
A vector indicating which model is being evaluated |
locations.vec |
A vector giving the location corresponding to each entry in observations.vec |
year.vec |
A vector giving the year corresponding toeach entry in observations.vec |
forecast.target |
The quantity being forecast. Forecasts targets are:
|
threshold |
For continuous and discrete forecasts, a threshold of error to be used in classifying the forecast as "accurate". |
percentage |
For continuous and discrete forecasts, if the prediction is within the specified percentage of the observed value, the forecast is considered accurate. |
accuracy.metrics A list containing accuracy information. The list is structured
RMSE | Root Mean Squared Error |
Scaled_RMSE | RMSE scaled by the mean observed value |
within_percentage | A binary accuracy classification, where a forecast within a specified percentage of the observation is considered accurate, otherwise it is inaccurate. |
within_threshold | A binary accuracy classification, where a forecast within a specified threshold from the observation is considered accurate, otherwise it is inaccurate |
within_threshold_or_percentage | A binary accuracy classification, where if it is accurate by either the threshold or percentage approaches, it is considered accurate. |
AUC | Area Under the Curve from the Receiver Operating Characteristic Plot |
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