
Get default metrics for multivariate point forecasts
Source:R/class-forecast-multivariate-point.R
get_metrics.forecast_multivariate_point.RdFor multivariate point forecasts, the default scoring rule is:
"variogram_score" =
variogram_score_multivariate_point()
Usage
# S3 method for class 'forecast_multivariate_point'
get_metrics(x, select = NULL, exclude = NULL, ...)Arguments
- x
A forecast object (a validated data.table with predicted and observed values, see
as_forecast_binary()).- select
A character vector of scoring rules to select from the list. If
selectisNULL(the default), all possible scoring rules are returned.- exclude
A character vector of scoring rules to exclude from the list. If
selectis notNULL, this argument is ignored.- ...
unused
Examples
data <- data.frame(
observed = c(1, 2, 3),
predicted = c(1.1, 2.2, 3.3),
target = c("a", "b", "c"),
model = "m1",
date = "2020-01-01"
)
ex <- as_forecast_multivariate_point(
data,
forecast_unit = c("model", "date", "target"),
joint_across = "target"
)
get_metrics(ex)
#> $variogram_score
#> function (observed, predicted, mv_group_id, w_vs = NULL, p = 0.5)
#> {
#> assert_numeric(observed, min.len = 1)
#> assert_numeric(as.vector(predicted), min.len = 1)
#> assert_numeric(mv_group_id, len = length(observed))
#> unique_groups <- unique(mv_group_id)
#> vs <- vapply(unique_groups, function(group) {
#> idx <- which(mv_group_id == group)
#> scoringRules::vs_sample(y = observed[idx], dat = predicted[idx,
#> , drop = FALSE], w_vs = w_vs, p = p)
#> }, numeric(1))
#> names(vs) <- unique_groups
#> return(vs)
#> }
#> <bytecode: 0x56291d316ad0>
#> <environment: namespace:scoringutils>
#>
