It estimates the agreement coefficient (lambda) suggested by Duveiller et al. (2016) for a continuous predicted-observed dataset.

## Usage

lambda(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

lambda measures both accuracy and precision. It is normalized, dimensionless, bounded (-1;1), and symmetric (invariant to predicted-observed orientation). lambda is equivalent to CCC when r is greater or equal to 0. The closer to 1 the better. Values towards zero indicate low correlation between observations and predictions. Negative values would indicate a negative relationship between predicted and observed. For the formula and more details, see online-documentation

## References

Duveiller et al. (2016). Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. 6, 1-14. doi:10.1038/srep19401

## Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- rnorm(n = 100, mean = 0, sd = 9)
lambda(obs = X, pred = Y)
#> \$lambda
#> [1] -0.0009804333
#>
# }