reliability {triptych} | R Documentation |
Evaluation of forecasts using reliability curves
Description
A reliability curve visualizes miscalibration by displaying the (isotonic) conditional event probability against the forecast value.
Usage
reliability(x, y_var = "y", ..., y = NULL)
as_reliability(x, r)
Arguments
x |
A data frame, list, matrix, or other object that can be coerced to a tibble. Contains numeric forecasts, and observations (optional). |
y_var |
A variable in |
... |
Unused. |
y |
A numeric vector of observations. If supplied, overrides |
r |
A reference triptych_mcbdsc object whose attributes are used for casting. |
Value
A triptych_reliability
object, that is a vctrs_vctr
subclass, and has
a length equal to number of forecasting methods supplied in x
. Each entry
is named according to the corresponding forecasting method,
and contains a list of named objects:
-
estimate
: A data frame with the isotonic regression estimate. -
region
: Either an empty list, or a data frame of pointwise consistency or confidence intervals added byadd_consistency()
oradd_confidence()
, respectively. -
x
: The numeric vector of original forecasts.
Access is most convenient through estimates()
, regions()
, and forecasts()
.
See Also
Accessors: estimates()
, regions()
, forecasts()
, observations()
Adding uncertainty quantification: add_confidence()
Visualization: plot.triptych_reliability()
, autoplot.triptych_reliability()
Examples
data(ex_binary, package = "triptych")
rel <- reliability(ex_binary)
rel
# 1. Choose 4 predictions
# 2. Visualize
# 3. Adjust the title of the legend
rel[c(1, 3, 6, 9)] |>
autoplot() +
ggplot2::guides(colour = ggplot2::guide_legend("Forecast"))
# Build yourself using accessors
library(ggplot2)
df_est <- estimates(rel[c(1, 3, 6, 9)])
ggplot(df_est, aes(x = x, y = CEP, col = forecast)) +
geom_segment(aes(x = 0, y = 0, xend = 1, yend = 1)) +
geom_path()