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, predicted can just be a vector of size n.

Input format

Examples

observed <- rnorm(30, mean = 1:30)
predicted_values <- matrix(rnorm(30, mean = 1:30))
se_mean_sample(observed, predicted_values)
#>  [1]  0.014829896  0.316608439  1.142566519  0.086187944  0.058926355
#>  [6]  3.073734463  0.002511154  0.008577179  3.999907031  1.048431205
#> [11]  0.281251491  2.816708187 12.593948837  1.990408824  0.123815451
#> [16]  0.569334753  1.956832571  0.052113786  1.198119654  0.285800371
#> [21]  0.113974757  8.105728298  0.019562959  0.737141759  0.375735782
#> [26]  0.062009545  0.058334996  0.492055176  0.007062469  0.978271085