weighted.loss() computes various loss metrics (e.g., RMSE, MAE) between two numeric vectors, or for the deviations from the weighted mean of a numeric vector.
Usage
weighted.loss(
x,
y = NULL,
w = NULL,
na.rm = FALSE,
method = c("rmse", "mse", "mae", "medae", "r2")
)Arguments
- x
a numeric vector.
- y
an optional numeric vector. If
NULL,xis compared against its weighted mean.- w
a numeric vector of sample weights for each value in
x.- na.rm
logical. If
TRUE, anyNAandNaNs are removed from all input vectors before the calculation.- method
the loss measure. One of "mse" (mean square error), "rmse" (root mean square error), mae" (mean absolute error), "medae" (median absolute error), or "r2" (R-squared).
Examples
# Calculate loss metrics between x and y with weights
weighted.loss(x = c(0, 10), y = c(0, 0), w = c(99, 1), method = "rmse")
#> [1] 1
weighted.loss(x = c(0, 10), y = c(0, 0), w = c(99, 1), method = "mae")
#> [1] 0.1
weighted.loss(x = c(0, 10), y = c(0, 0), w = c(99, 1), method = "medae")
#> [1] 0
# Verify uninterpreted variation ratio of a fitted MID model without weights
mid <- interpret(dist ~ speed, cars)
#> 'model' not passed: response variable in 'data' is used
1 - weighted.loss(cars$dist, predict(mid, cars), method = "r2")
#> [1] 0.2263636
mid$ratio
#> [1] 0.2263636
# Verify uninterpreted variation ratio of a fitted MID model with weights
w <- 1:nrow(cars)
mid <- interpret(dist ~ speed, cars, weights = w)
#> 'model' not passed: response variable in 'data' is used
1 - weighted.loss(cars$dist, predict(mid, cars), w = w, method = "r2")
#> [1] 0.2864814
mid$ratio
#> [1] 0.2864814