www.gusucode.com > stats 源码程序 matlab案例代码 > stats/ThreeWayANOVAMultipleComparisonsExample.m
%% Multiple Comparisons for Three-Way ANOVA % Load the sample data. % Copyright 2015 The MathWorks, Inc. y = [52.7 57.5 45.9 44.5 53.0 57.0 45.9 44.0]'; g1 = [1 2 1 2 1 2 1 2]; g2 = {'hi';'hi';'lo';'lo';'hi';'hi';'lo';'lo'}; g3 = {'may';'may';'may';'may';'june';'june';'june';'june'}; %% % |y| is the response vector and |g1|, |g2|, and |g3| are the grouping % variables (factors). Each factor has two levels, and every observation in % |y| is identified by a combination of factor levels. For example, % observation |y(1)| is associated with level 1 of factor |g1|, level % |'hi'| of factor |g2|, and level |'may'| of factor |g3|. Similarly, % observation |y(6)| is associated with level 2 of factor |g1|, level % |'hi'| of factor |g2|, and level |'june'| of factor |g3|. %% % Test if the response is the same for all factor levels. Also compute the % statistics required for multiple comparison tests. [~,~,stats] = anovan(y,{g1 g2 g3},'model','interaction',... 'varnames',{'g1','g2','g3'}); %% % The _p_-value of 0.2578 indicates that the mean responses for levels % |'may'| and |'june'| of factor |g3| are not significantly different. % The _p_-value of 0.0347 indicates that the mean responses for levels |1| % and |2| of factor |g1| are significantly different. Similarly, the % _p_-value of 0.0048 indicates that the mean responses for levels |'hi'| % and |'lo'| of factor |g2| are significantly different. %% % Perform multiple comparison tests to find out which groups of the factors % |g1| and |g2| are significantly different. results = multcompare(stats,'Dimension',[1 2]) %% % <docid:stats_ug.bujc800 multcompare> compares the combinations of groups % (levels) of the two grouping variables, |g1| and |g2|. In the |results| % matrix, the number 1 corresponds to the combination of level |1| of |g1| % and level |hi| of |g2|, the number 2 corresponds to the combination of % level |2| of |g1| and level |hi| of |g2|. Similarly, the number 3 % corresponds to the combination of level |1| of |g1| and level |lo| of % |g2|, and the number 4 corresponds to the combination of level |2| of % |g1| and level |lo| of |g2|. The last column of the matrix contains the % _p_-values. % % For example, the first row of the matrix shows that the combination of % level |1| of |g1| and level |hi| of |g2| has the same mean response % values as the combination of level |2| of |g1| and level |hi| of |g2|. % The _p_-value corresponding to this test is 0.0280, which indicates % that the mean responses are significantly different. You can also see this result in the % figure. The blue bar shows the comparison interval for the mean % response for the combination of level |1| of |g1| and level |hi| of |g2|. % The red bars are the comparison intervals for the mean response for % other group combinations. None of the red bars overlap with the blue bar, % which means the mean response for the combination of level |1| of |g1| % and level |hi| of |g2| is significantly different from the mean response % for other group combinations. % % You can test the other groups by clicking on the corresponding comparison % interval for the group. The bar you click on turns to blue. The bars for % the groups that are significantly different are red. The bars for the % groups that are not significantly different are gray. For example, if you % click on the comparison interval for the combination of level |1| of |g1| % and level |lo| of |g2|, the comparison interval for the combination of % level |2| of |g1| and level |lo| of |g2| overlaps, and is therefore gray. % Conversely, the other comparison intervals are red, indicating % significant difference.