How do you print the F-stat in a multcompare test?

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Heads up, I'm a MATLAB novice. I'm trying to run a Fisher's test on a one-way ANOVA that I conducted comparing the difference in 3 therapy groups. How do I print out the F-statistic for this multiple comparisons test?
The code I'm using:
%data input
ingroups = [5,4,6,7,5,6,7,5,5,6;6,7,7,5,7,7,8,6,7,7;7,8,7,7,8,5,6,7,7,7];
Mon = [5 4 6 7 5 6 7 5 5 6];
Bon = [6 7 7 5 7 7 8 6 7 7];
Feel = [7 8 7 7 8 5 6 7 7 7];
%transpose data rows in to columns for anova1
ingroups2 = transpose(ingroups);
%one-way ANOVA (treats columns as separate groups)
[p,tbl,stats] = anova1(ingroups2);
%Fisher's protected test (least significant difference)
[c,m,h,gnames] = multcompare(stats,'CType','lsd');

Accepted Answer

Adam Danz
Adam Danz on 15 Sep 2021
You are performing a one-way ANOVA which means your null hypothesis is that there is no difference in the population means of your 3 levels of some factor. This assumes the data in each factor are normally distributed. Below is a quick eye-ball check of that assumption. It's difficult to call these data normally distributed since you have so few data and they are intergers within a narrow range. If these are your real data, I would not be comfortable confirming the normality assumption and therefore I would not be comfortable relying on any parametric test including an ANOVA.
ingroups = [5,4,6,7,5,6,7,5,5,6;6,7,7,5,7,7,8,6,7,7;7,8,7,7,8,5,6,7,7,7]'; % Notice transpose!
nexttile(); histogram(ingroups(:,1)); title('Column 1')
nexttile(); histogram(ingroups(:,2)); title('Column 2')
nexttile(); histogram(ingroups(:,3)); title('Column 3')
Assuming the data provided are example data and that your real data are approximately normally distributed, I'll move on to your original question. The Fisher Exact Test is designed for 2x2 contingency tables. Since you have 3 levels, you'd likely want to use a chi-squared test instead. See Data Science Blog and Analysis of Categorical Data (PDF) for more info.
If you're just trying to get the F-statistic from the ANOVA table,
[p,tbl,stats] = anova1(ingroups,[],'off');
tbl(cellfun('isempty',tbl)) = {nan};
T = cell2table(tbl(2:end,:), 'VariableNames', tbl(1,:))
T = 3×6 table
Source SS df MS F Prob>F ___________ ____ __ _______ ______ _________ {'Columns'} 9.8 2 4.9 6.1822 0.0061588 {'Error' } 21.4 27 0.79259 NaN NaN {'Total' } 31.2 29 NaN NaN NaN
F = T.F(1)
F = 6.1822

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More Answers (1)

Scott MacKenzie
Scott MacKenzie on 19 Sep 2021
@Darla Bonagura There is no F-statistic for post hoc pairwise comparisons tests such as Bonferonni, Scheffe, and so on. There is, of course, an F-statistic for the ANOVA that precedes the post hoc test. The post hoc tests work by comparing the difference in each pair of test conditions with a critical value (computed in different ways for each test). The critical value is computed with a desired significance value, which is most often p < .05. If that difference is greater than the critical value, those two conditions are said to differ significantly (p < .05).

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