Squared error of the mean (sample-based version)

Squared error of the mean calculated as

$$ \textrm{mean}(\textrm{observed} - \textrm{mean prediction})^2 $$ The mean prediction is calculated as the mean of the predictive samples.

Usage

se_mean_sample(observed, predicted)

Arguments

observed

A vector with observed values of size n

predicted

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, if n = 1, predicted can just be a vector of size n.

Input format

Overview of required input format for sample-based forecasts

Examples

observed <- rnorm(30, mean = 1:30)
predicted_values <- matrix(rnorm(30, mean = 1:30))
se_mean_sample(observed, predicted_values)
#>  [1] 1.385961359 2.840631832 0.563788699 0.356389380 0.858331477 3.606268887
#>  [7] 0.157880074 6.666548840 2.816835943 0.562024085 0.484509868 3.809361150
#> [13] 0.368769078 1.570164233 2.714350780 1.512258863 2.207051334 2.724803527
#> [19] 0.866446251 0.034394909 1.406837546 0.005078808 3.812047069 5.737942798
#> [25] 1.490066803 3.471397540 2.612408037 0.930702120 0.219383033 5.971547387