0todd0000
commented
1 month ago Apologies for the delay! I missed your question in my inbox. For anova1rm paired t tests are an appropriate post hoc procedure, so I suggest conducting paired post hoc tests using spm1d.stats.ttest_paired, and adjusting the critical p-value for the number of pairs.
I want to conduct a post hoc following the non parametric 1d_anova1rm. however I struggle to find a good way to do this. Can you help?
The code I used for the anova is below. Y= knee flexion during three different conditions. Therefore the post hoc must compare the three conditions pairwise.
"" clear; clc
% Last inn data fra Excel-filene Y = importdata('Matlab1.xlsx'); A = importdata('Matlab2.xlsx'); SUBJ = importdata('Matlab3.xlsx');
% Forbered en tom matrise for dobbeltverdier Y_double = zeros(size(Y));
% Konverter strengene til dobbeltverdier for i = 1:numel(Y) Y_double(i) = str2double(Y{i});
end
%(1) Conduct non-parametric test: rng(0) alpha = 0.05; iterations = 100; snpm = spm1d.stats.nonparam.anova1rm(Y_double, A, SUBJ); snpmi = snpm.inference(alpha, 'iterations', iterations); disp('Non-Parametric results') disp( snpmi )
%(2) Compare to parametric inference: spm = spm1d.stats.anova1rm(Y_double, A, SUBJ); spmi = spm.inference(alpha); disp('Parametric results') disp( spmi ) % plot: close all
% Plot med x-aksen fra 0 til 100 figure; spmi.plot(); hold on
% Begrens x-aksen til 0, 50 og 100 xticks([0, 25, 50, 75, 100]); yticks([0, 60, 120, 180]);
title('SPM ANOVA', 'FontSize', 18); xlabel('Time (%)');
% Plot med x-aksen fra 0 til 100 figure; snpmi.plot(); hold on
% Begrens x-aksen til 0, 50 og 100 xticks([0, 25, 50, 75, 100]); yticks([0, 60, 120, 180]);
title('SPM ANOVA', 'FontSize', 18); xlabel('Time (%)'); ""
Thank you