Lack of Consistency (Ue) — Ue • metrica

It estimates the Ue component from the sum of squares decomposition described by Smith & Rose (1995).

Usage

Ue(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). The Ue estimates the proportion of the total sum of squares related to the random error (unsystematic error or variance) following the sum of squares decomposition suggested by Smith and Rose (1995) also known as Theil's partial inequalities. For the formula and more details, see online-documentation

References

Smith & Rose (1995). Model goodness-of-fit analysis using regression and related techniques. Ecol. Model. 77, 49–64. doi:10.1016/0304-3800(93)E0074-D

Examples

# \donttest{
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
Ue(obs = X, pred = Y)
#> $Ue
#> [1] 97.44035
#> 
# }