murphy {triptych} | R Documentation |
Evaluation of forecasts using Murphy curves
Description
A Murphy curve visualizes economic utility by displaying the mean elementary scores across all threshold values.
Usage
murphy(x, y_var = "y", ref_var = "ref", ..., y = NULL, ref = NULL)
as_murphy(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 |
ref_var |
A variable in |
... |
Unused. |
y |
A numeric vector of observations. If supplied, overrides |
ref |
A numeric vector of reference forecasts. If supplied, overrides |
r |
A reference triptych_murphy object whose attributes are used for casting. |
Value
A triptych_murphy
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 threshold and corresponding mean score values. -
region
: Either an empty list, or a data frame of point confidence intervals added byadd_confidence()
. -
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_murphy()
, autoplot.triptych_murphy()
Examples
data(ex_binary, package = "triptych")
mr <- murphy(ex_binary)
mr
# 1. Choose 4 predictions
# 2. Visualize
# 3. Adjust the title of the legend
mr[c(1, 3, 6, 9)] |>
autoplot() +
ggplot2::guides(colour = ggplot2::guide_legend("Forecast"))
# Build yourself using accessors
library(ggplot2)
df_est <- estimates(mr[c(1, 3, 6, 9)])
ggplot(df_est) +
geom_path(aes(x = knot, y = mean_score, col = forecast))