61 lines
1.9 KiB
Matlab
61 lines
1.9 KiB
Matlab
function [cleanedTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var)
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% removeGroupOutliers removes outliers in y_var within each group defined by fixedVars.
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%
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% [cleanedTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var)
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%
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% Inputs:
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% dataTable - Input MATLAB table
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% fixedVars - Cell array of variable names to group by
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% y_var - Name of the variable to check for outliers (string or char)
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%
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% Outputs:
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% cleanedTable - Table with outliers removed
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% outliersTable - Table of removed outlier rows
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[G, groupKeys] = findgroups(dataTable(:, fixedVars));
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keepIdx = true(height(dataTable), 1);
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outlierRecords = [];
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for groupIdx = 1:height(groupKeys)
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groupRows = (G == groupIdx);
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y_values = dataTable.(y_var)(groupRows);
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% Skip groups with fewer than 3 points
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if sum(groupRows) < 3
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continue;
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end
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% Detect outliers in log10 space (robust for BER, etc.)
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y_log = log10(y_values);
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outlierMask = isoutlier(y_log, 'quartiles', 1);
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if any(outlierMask)
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groupData = dataTable(groupRows, :);
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outlierGroupTable = groupData(outlierMask, :);
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% Optionally, add group key values for traceability
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for k = 1:numel(fixedVars)
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outlierGroupTable.(['Group_', fixedVars{k}]) = repmat(groupKeys{groupIdx, k}, height(outlierGroupTable), 1);
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end
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outlierRecords = [outlierRecords; outlierGroupTable]; %#ok<AGROW>
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end
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% Mark outliers for removal
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groupRowIdx = find(groupRows);
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keepIdx(groupRowIdx(outlierMask)) = false;
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end
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cleanedTable = dataTable(keepIdx, :);
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if isempty(outlierRecords)
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outliersTable = table();
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else
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outliersTable = outlierRecords;
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end
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nRemoved = sum(~keepIdx);
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nTotalOriginal = height(dataTable);
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fprintf('Removed %d outliers from the data table (%.2f%% of total %d entries).\n', ...
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nRemoved, 100*nRemoved/nTotalOriginal, nTotalOriginal);
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end |