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

## 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