Hi Luk,
You can use the "anovan" function with your data to achieve the desired analysis. You can first prepare the data for anova analysis, apply the "anovan" function, and then use "multcompare" to perform multiple comparisons on different groups.
Here is a sample code for your reference:
% Data
A = [1 0 1 0 0 1 0];
B = [1 1 0 0 1 1 1];
LabelsA = {'red', 'blue', 'white', 'white', 'white', 'blue', 'red'};
LabelsB = {'high', 'medium', 'medium', 'high', 'low', 'low', 'high'};
% Combine data into a single vector and create group labels
responses = [A, B];
group1 = [LabelsA, LabelsB]; % Combine labels for A and B
group2 = [repmat({'A'}, 1, length(A)), repmat({'B'}, 1, length(B))]; % Indicate source (A or B)
% Perform n-way ANOVA
[p, tbl, stats] = anovan(responses, {group1, group2}, 'model', 'interaction', 'varnames', {'Label', 'Source'});
% Display ANOVA table
disp(tbl);
% Perform multiple comparisons
results = multcompare(stats, 'Dimension', [1 2]); % Compare across both dimensions
% Display multiple comparison results
disp(results);
% Plot interaction plot for visual analysis
figure;
interactionplot(responses, {group1, group2}, 'varnames', {'Label', 'Source'});
The above code errors out in the "interactionplot" due to missing combinations of factor levels. It can happen when the random data doesn't cover all possible combinations of the factors, which is common with small datasets, or when factors are not fully crossed.
Please refer to the following documentation links for more information:
- anovan: https://www.mathworks.com/help/stats/anovan.html.
- multcompare: https://www.mathworks.com/help/stats/multcompare.html.
- interactionplot: https://www.mathworks.com/help/stats/interactionplot.html.
I hope this resolves your issue!
