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Accuracy indices

{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:

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

Element-wise

Error: error()

It simply subtracts the predicted from the actual values.

Formula: \[a_i - p_i\]

Absolute error: error_abs()

It returns the absolute values of the error() function.

Formula: \[|a_i - p_i|\]

Percent error: error_pct()

Divides the error by the actual values.

Formula: \[\frac{a_i - p_i}{a_i}\cdot100\]

Absolute percent error: error_abs_pct()

Returns the absolute values of the error_pct() function.

Formula: \[\frac{|a_i - p_i|}{|a_i|}\cdot100\]

Squared error: error_sqr()

It squares the values of the error() function.

Formula: \[(a_i - p_i)^2\]

Average

Mean error: mean_error()

It is the average of the error.

Formula: \[\frac{1}{N}\sum_{i = 1}^{N}(a_i - p_i)\]

Mean absolute error: mean_error_abs()

Computes the average of the absolute error.

Formula: \[\frac{1}{N}\sum_{i = 1}^{N}|a_i - p_i|\]

Mean percent error: 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 absolute percent error: 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 squared error: mean_error_sqr()

Averages the mean squared error.

Formula: \[\frac{1}{N}\sum_{i = 1}^{N}(a_i - p_i)^2\]

Root mean squared error: 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: bias()

Alias to mean_error().

Limits of agreement: loa()

Formula: \[bias \pm 1.96\sigma\]

Where \(\sigma\) is the standard deviation.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.