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Squared Error of the Mean (Sample-based Version)

Source: R/metrics_point_forecasts.R
se_mean_sample.Rd

Squared error of the mean calculated as

$$ \textrm{mean}(\textrm{true\_value} - \textrm{prediction})^2 $$

Usage

se_mean_sample(true_values, predictions)

Arguments

true_values

A vector with the true observed values of size n

predictions

nxN matrix of predictive samples, n (number of rows) being the number of data points and N (number of columns) the number of Monte Carlo samples. Alternatively, predictions can just be a vector of size n.

Value

vector with the scoring values

See also

squared_error()

Examples

true_values <- rnorm(30, mean = 1:30)
predicted_values <- rnorm(30, mean = 1:30)
se_mean_sample(true_values, predicted_values)
#>  [1]  0.77394841  1.01111794 11.02912184  4.90532223  2.56020025  0.02195793
#>  [7]  2.76090589  2.03635257  0.12537056  0.51296868  4.61605491  0.33748314
#> [13]  0.14674311  4.86501849 23.11811168  2.72727320  0.90166468 10.79581353
#> [19]  0.34990149  1.30024405  1.36306936  5.62752202  0.52834759  0.95203665
#> [25]  4.83326989  1.56527458  0.96086778  0.02214336  2.43114610  0.41195893