knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
{lvmisc} contains a group of useful functions to compute basic indices of accuracy. These functions can be divided in those which compute element-wise values and those which compute average values:
Element-wise:
error()
error_abs()
error_pct()
error_abs_pct()
error_sqr()
Average:
mean_error()
mean_error_abs()
mean_error_pct()
mean_error_abs_pct()
mean_error_sqr()
mean_error_sqr_root()
bias()
loa()
You may notice that the majority of these functions have common prefixes (error_
and mean_error_
), intended to facilitate the use, as most text editors have an auto-complete feature. Also all of the accuracy indices functions take actual
and predicted
as arguments, and the functions that return average values have na.rm = TRUE
in addition.
Let's now see how each function computes its results
error()
It simply subtracts the predicted
from the actual
values.
Formula: $$a_i - p_i$$
error_abs()
It returns the absolute values of the error()
function.
Formula: $$|a_i - p_i|$$
error_pct()
Divides the error by the actual
values.
Formula: $$\frac{a_i - p_i}{a_i}\cdot100$$
error_abs_pct()
Returns the absolute values of the error_pct()
function.
Formula: $$\frac{|a_i - p_i|}{|a_i|}\cdot100$$
error_sqr()
It squares the values of the error()
function.
Formula: $$(a_i - p_i)^2$$
mean_error()
It is the average of the error.
Formula: $$\frac{1}{N}\sum_{i = 1}^{N}(a_i - p_i)$$
mean_error_abs()
Computes the average of the absolute error.
Formula: $$\frac{1}{N}\sum_{i = 1}^{N}|a_i - p_i|$$
mean_error_pct()
The average of the percent error.
Formula: $$\frac{1}{N}\sum_{i = 1}^{N}\frac{a_i - p_i}{a_i}\cdot100$$
mean_error_abs_pct()
It is the average of the absolute percent error.
Formula: $$\frac{1}{N}\sum_{i = 1}^{N}\frac{|a_i - p_i|}{|a_i|}\cdot100$$
mean_error_sqr()
Averages the mean squared error.
Formula: $$\frac{1}{N}\sum_{i = 1}^{N}(a_i - p_i)^2$$
mean_error_sqr_root()
It takes the square root of the mean squared error.
Formula: $$\sqrt{\frac{1}{N}\sum_{i = 1}^{N}(a_i - p_i)^2}$$
bias()
Alias to mean_error()
.
loa()
Formula: $$bias \pm 1.96\sigma$$
Where $\sigma$ is the standard deviation.
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