It estimates the RMLA, the square root of the systematic error component to the Mean Squared Error (MSE), for a continuous predicted-observed dataset following Correndo et al. (2021).

## Usage

RMLA(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 RMLA represents the systematic (bias) component of the MSE expressed into the original variable units. It is obtained via a symmetric decomposition of the MSE (invariant to predicted-observed orientation) using a symmetric regression line (SMA). The RMLA is equal to the square-root of the sum of systematic differences divided by the sample size (n). The greater the value the greater the bias of the predictions. For the formula and more details, see online-documentation

## References

Correndo et al. (2021). Revisiting linear regression to test agreement in continuous predicted-observed datasets. Agric. Syst. 192, 103194. doi:10.1016/j.agsy.2021.103194

## Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
RMLA(obs = X, pred = Y)
#> \$RMLA
#> [1] 0.2095862
#>
# }