It estimates the RMSE for a continuous predicted-observed dataset.

## Usage

RMSE(data = NULL, obs, pred, tidy = FALSE, na.rm = TRUE)

## Arguments

data

(Optional) argument to call an existing data frame containing the data.

obs

Vector with observed values (numeric).

pred

Vector with predicted values (numeric).

tidy

Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.

na.rm

Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.

## Value

an object of class numeric within a list (if tidy = FALSE) or within a data frame (if tidy = TRUE).

## Details

The RMSE is one of the most widely used error metrics in literature to evaluate prediction performance. It measures general agreement, being sensitive to both lack of precision and lack of accuracy. It is simply the square root of the Mean Squared Error (MSE). Thus, it presents the same units as the variable of interest, facilitating the interpretation. It goes from 0 to infinity. The lower the value the better the prediction performance. Its counterpart is being very sensitive to outliers. For the formula and more details, see online-documentation

## Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
RMSE(obs = X, pred = Y)
#> \$RMSE
#> [1] 2.861482
#>
# }