Replace mMEANERROR with mABSERROR

mABSERROR.m

@@ -0,0 +1,56 @@
+function [RESULT] = mABSERROR(DATA, NORMALIZER)
+% Calculate mean absolute error with different normalization options
+%
+%   DATA is a numeric matrix where each row is one object, the first column
+%   contains the test results (predictions), and the second column contains
+%   the criterion results (trusted labels). Note that this function is most
+%   appropriate when the data being compared is dimensional.
+%
+%   NORMALIZER is an optional parameter that specifies what the mean
+%   absolute error value should be divided by (default = 1). Valid options
+%   include 'range', 'mean', 'std', and any real number. If 'range',
+%   'mean', or 'std' is chosen, the values are calculated from the
+%   observed criterion results (i.e., the second column of DATA).
+%
+%   RESULT is a scalar containing the mean absolute error if NORMALIZER
+%   was set to 1, or the normalized mean absolute error otherwise.
+%
+%   Example usage: mABSERROR([preds, labels])
+%   Example usage: mABSERROR([preds, labels], 'range')
+%   Example usage: mABSERROR(readydata, 50.23)
+%
+%   (c) Jeffrey M. Girard, 2018
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isnumeric(DATA)
+    disp('DATA must be a numeric matrix');
+    RESULT = NaN;
+    return;
+end
+
+if size(DATA, 2) ~= 2
+    disp('DATA must have 2 columns');
+    RESULT = NaN;
+    return;
+end
+
+if nargin < 2
+    NORMALIZER = 1;
+end
+
+TEST = DATA(:, 1);
+CRIT = DATA(:, 2);
+
+if strcmpi(NORMALIZER, 'range')
+    NORMALIZER = range(CRIT);
+elseif strcmpi(NORMALIZER, 'mean')
+    NORMALIZER = nanmean(CRIT);
+elseif strcmpi(NORMALIZER, 'std')
+    NORMALIZER = nanstd(CRIT);
+end
+
+MAE = nanmean(abs(CRIT - TEST));
+RESULT = MAE / NORMALIZER;
+
+end