PCC {SeaVal}

Pearson Correlation Coefficient

Description

Function for calculating Pearson correlation coefficients (PCCs) of ensemble mean forecasts stored in long data tables. Can also handle point forecasts. This metric always needs several years of data since the means and standard deviations are calculated across time.

Usage

PCC(
  dt,
  f,
  o = "obs",
  by = by_cols_ens_fc_score(dt),
  pool = "year",
  mem = "member",
  dim.check = TRUE
)

Arguments

dt

Data table containing the predictions.

f

column name of the prediction.

o

column name of the observations.

by

column names of grouping variables, all of which need to be columns in dt. A separate PCC is computed for each value of the grouping variables. 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. Needs to contain 'year' per warning above.

mem

Name of the column identifying the ensemble member. Only used if check_dimension is TRUE. Is NULL for a point forecast.

dim.check

Logical. If True, a simple test whether the dimensions match up is conducted: The data table should only have one row for each level of c(by,pool,mem)

Value

A data table with the scores

Examples

dt = data.table(fc = 1:4,obs = c(4,4,7,7),member = c(1,2,1,2),year = c(1999,1999,2000,2000))
PCC(dt,f = 'fc')