Fit Linear Model Using "fitlm(" With 3-Way and 4-Way Interactions

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Sarah
Sarah on 5 Dec 2018
I want to create a linear regression model with 2, 3, and 4-way interactions. However, currently, my code will only return a 2-way interaction. How do I get the 3-way and 4-way interactions?
Here's my current code and the results:
y =[45; 71; 48; 65; 68; 60; 80; 65; 43; 100; 45; 104; 75; 86; 70; 96];
g1 = {'−'; '+'; '−'; '+'; '−'; '+'; '−'; '+'; '−'; '+'; '−'; '+'; '−'; '+'; '−'; '+'};
g2 = {'−'; '−'; '+'; '+'; '−'; '−'; '+'; '+'; '−'; '−'; '+'; '+'; '−'; '−'; '+'; '+'};
g3 = {'−'; '−'; '−'; '−'; '+'; '+'; '+'; '+'; '−'; '−'; '−'; '−'; '+'; '+'; '+'; '+'};
g4 = {'−'; '−'; '−'; '−'; '−'; '−'; '−'; '−'; '+'; '+'; '+'; '+'; '+'; '+'; '+'; '+'};
tbl = table(y,g1,g2,g3,g4);
lm = fitlm(tbl, 'interactions')
lm =
Linear regression model:
y ~ 1 + g1*g2 + g1*g3 + g1*g4 + g2*g3 + g2*g4 + g3*g4
Estimated Coefficients:
Estimate SE tStat pValue
________ ______ _________ __________
(Intercept) 45.188 4.1922 10.779 0.00011917
g1_+ 23 5.0559 4.5491 0.0061175
g2_+ 1 5.0559 0.19779 0.851
g3_+ 26.75 5.0559 5.2908 0.0032162
g4_+ -0.5 5.0559 -0.098894 0.92507
g1_+:g2_+ 0.25 5.0559 0.049447 0.96248
g1_+:g3_+ -36.25 5.0559 -7.1698 0.00082085
g1_+:g4_+ 33.25 5.0559 6.5764 0.00122
g2_+:g3_+ 4.75 5.0559 0.93949 0.39061
g2_+:g4_+ -0.75 5.0559 -0.14834 0.88787
g3_+:g4_+ -2.25 5.0559 -0.44502 0.67491
Number of observations: 16, Error degrees of freedom: 5
Root Mean Squared Error: 5.06
R-squared: 0.978, Adjusted R-Squared 0.933
F-statistic vs. constant model: 21.9, p-value = 0.00163

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