metrics_summary function

Prediction Performance Summary

It estimates a group of metrics characterizing the prediction performance for a continuous (regression) or categorical (classification) predicted-observed dataset. By default, it calculates all available metrics for either regression or classification.

metrics_summary(
  data = NULL,
  obs,
  pred,
  type = NULL,
  metrics_list = NULL,
  orientation = "PO",
  pos_level = 2,
  na.rm = TRUE
)

Arguments

• data: argument to call an existing data frame containing the data (optional).
• obs: vector with observed values (numeric).
• pred: vector with predicted values (numeric).
• type: argument of class string specifying the type of model. For continuous variables use type = 'regression'. For categorical variables use type = 'classification'.
• metrics_list: vector or list of specific selected metrics. Default is = NULL, which will estimate all metrics available for either regression or classification.
• orientation: argument of class string specifying the axis orientation to estimate slope(B1) and intercept(B0). It only applies when type = "regression". "PO" is for predicted vs observed, and "OP" for observed vs predicted. Default is orientation = "PO".
• pos_level: (for classification only). Integer, for binary cases, indicating the order (1|2) of the level corresponding to the positive. Generally, the positive level is the second (2) since following an alpha-numeric order, the most common pairs are (Negative | Positive), (0 | 1), (FALSE | TRUE). Default : 2.
• na.rm: Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.

Returns

an object of class data.frame containing all (or selected) metrics.

Details

The user can choose to calculate a single metric, or to calculate all metrics at once. This function creates a data.frame with all (or selected) metrics in the metrica-package. If looking for specific metrics, the user can pass a list of desired metrics using the argument “metrics_list” (e.g. metrics_list = c("R2","MAE", "RMSE", "RSR", "NSE", "KGE")). For the entire list of available metrics with formula, see online-documentation

Examples

# Continuous variable (regression)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- rnorm(n = 100, mean = 0, sd = 10)
regression_case <- data.frame(obs = X, pred = Y)

# Get a metrics summary for a regression problem
metrics_summary(regression_case, obs = X, pred = Y, type = "regression")

# Categorical variable (classification)
binomial_case <- data.frame(labels = sample(c("True","False"), 100, 
replace = TRUE), predictions = sample(c("True","False"), 100, replace = TRUE))

#' # Get a metrics summary for a regression problem
metrics_summary(binomial_case, obs = labels, pred = predictions,
type = "classification")