accuracy: Calculate accuracy metrics
In mqueinnec/foster: Forest Structure Extrapolation with R

Description Usage Arguments Details Value Examples

Calculate coefficient of determination (R2), root-mean square error (RMSE) and bias between predictions and observations of continuous variables.

1	accuracy(obs, preds, vars = NULL, folds = NULL)

`obs`	A vector of observed values
`preds`	A vector of predicted values
`vars`	Optional vector indicating different variables
`folds`	Optional vector indicating the folds

R2 is calculated with the following formula:

R^{2} = 1 - \frac{∑ (y_{i} - \hat{y}_{i})^{2}}{∑ (y_{i} - \bar{y}_{i})^{2}}

RMSE is calculated with the following formula:

RMSE = √{\frac{1}{n} ∑ (\hat{y}_{i} - y_{i})^{2}}

Bias is calculated with the following formula:

Bias = \frac{∑ (\hat{y}_{i} - y_{i})}{n}

Relative RMSE and bias are also calculated by dividing their value by the mean of observations.

If accuracy assessment was performed using k-fold cross-validation the accuracy metrics are calculated for each fold separately. The mean value of the accuracy metrics across all folds is also returned.

Data frame with following columns:

vars: Response variable
R2: R2
RMSE: RMSE
RMSE_rel: Relative RMSE
bias: bias
bias_rel: Relative bias
count: Number of observations

# kNN_preds is a data frame obtained from foster::trainNN
# It contains predictions and observations of the trained kNN model
load(system.file("extdata/examples/kNN_preds.RData",package="foster"))

accuracy(obs = kNN_preds$obs,
         preds = kNN_preds$preds,
         vars = kNN_preds$variable,
         folds = kNN_preds$Fold)