SeaVal (version 1.1.1)

EIR: Effective Interest Rate

Description

This score is suitable for tercile category forecasts. Using log2 for now (?). According to Mason, the averaging here should be over many years at a single locations and for discrete time-periods (so Mason prefers to take the average after averaging over different locations, but I keep it like this for now).

Usage

EIR(
  dt,
  f = c("below", "normal", "above"),
  o = tc_cols(dt),
  by = by_cols_terc_fc_score(),
  pool = "year",
  dim.check = TRUE
)

Value

A data table with the scores

Arguments

dt

Data table containing the predictions.

f

column names of the prediction.

o

column name of the observations (either in obs_dt, or in dt if obs_dt = NULL). The observation column needs to contain -1 if it falls into the first category (corresponding to fcs[1]), 0 for the second and 1 for the third category.

by

column names of grouping variables, all of which need to be columns in dt. Default is to group by all instances of month, season, lon, lat, system and lead_time that are columns in dt.

pool

column name(s) for the variable(s) along which is averaged, typically just 'year'.

dim.check

Logical. If TRUE, the function tests whether the data table contains only one row per coordinate-level, as should be the case.

Examples

dt = data.table(below = c(0.5,0.3,0),
                normal = c(0.3,0.3,0.7),
                above = c(0.2,0.4,0.3),
                tc_cat = c(-1,0,0),
                lon = 1:3)
print(dt)
EIR(dt)