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Relative Squared Error (RSE)

Source: R/reg_RSE.R
RSE.Rd

It estimates the RSE for a continuous predicted-observer dataset.

Usage

RSE(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 RSE is the ratio between the residual sum of squares (RSS, error of predictions with respect to observations) and the total sum of squares (TSS, error of observations with respect to its mean). RSE is dimensionless, so it can be used to compared models with different units. 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)
RSE(obs = X, pred = Y)
#> $RSE
#> [1] 0.1025183
#> 
# }