Evaluation of forecasts using Murphy curves

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 x that contains observations. Specified as the argument varin dplyr::pull().

ref_var

A variable in x that contains reference forecasts. Specified as the argument var in dplyr::pull()). Ignored, if the default name is not present in x.

...

Unused.

y

A numeric vector of observations. If supplied, overrides y_var. Otherwise, defaults to dplyr::pull(x, y_var).

ref

A numeric vector of reference forecasts. If supplied, overrides ref_var. Otherwise, ignored (see ref_var) or dplyr::pull(x, ref_var).

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 by add_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
#> <triptych_murphy[10]>
#>             X01             X02             X03             X04             X05 
#> <named list[3]> <named list[3]> <named list[3]> <named list[3]> <named list[3]> 
#>             X06             X07             X08             X09             X10 
#> <named list[3]> <named list[3]> <named list[3]> <named list[3]> <named list[3]> 

# 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))