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Residual Sum of Squares (RSS)

Source: R/reg_RSS.R
RSS.Rd

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

Usage

RSS(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 RSS is the sum of the squared differences between predictions and observations. It represents the base of many error metrics using squared scale such as the Mean Squared Error (MSE). 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)
RSS(obs = X, pred = Y)
#> $RSS
#> [1] 818.8078
#> 
# }