0todd0000
commented
3 years ago Would I need to Bonferonni correct this? if so, how would I could about doing this?
A multiple comparisons correction is indeed necessary, but not necessarily the Bonferroni correction. If you use the Bonferroni, you could use it like this:
ntests = 12
p_critical = spm1d.util.p_critical_bonf(alpha, ntests)
posthoc_results = spm1d.stats.ttest_paired(variableA, variableB).inference(p_critical)Note that the Bonferroni correction is a conservative approximation and does not necessarily yield results that are fully consistent with the original ANOVA results.
One paper I read in JSCR did alpha/12 and another recent paper stated 'SPM has multiple comparison correction built in'
The built-in multiple comparisons correction is the random field theory (RFT) correction, which corrects across the 1D domain (usually time).
One more question if possible, to check where the shaded differences occur, I used the brush function to drag over it and visually inspect, is there a better way of doing this?
Yes, use the Clusters class, like this:
alpha = 0.05
t = spm1d.stats.ttest(Y, mu)
ti = t.inference(alpha, two_tailed=False, interp=True, circular=False)
print( ti.clusters )which yields something like this:
[Cluster
threshold : 3.227
centroid : (75.014, 3.535)
isinterpolated : True
iswrapped : False
endpoints : (72.822, 77.247)
extent : 4.425
extent (resels) : 0.16667
height (min) : 3.22704
P : 0.04441
]The endpoints attribute contains each cluster's endpoints along the 1D domain.
meechan1983
Hi Todd
I have started playing with SPM for a project I am working on comparing the effect of intensity. We ran an anova (40%,60%,80%,100%,120%,140%) and then multiple paired t tests independently ( 40vs60, 40vs80 and so on [ this results in a total of 15 load comparisons ). This was done by copying and pasting each load into variableA and variableB
Would I need to Bonferonni correct this? if so, how would I could about doing this? I have attached the code
One paper I read in JSCR did alpha/12 and another recent paper stated 'SPM has multiple comparison correction built in'
One more question if possible, to check where the shaded differences occur, I used the brush function to drag over it and visually inspect, is there a better way of doing this?
Many thanks in advance
%(1) Compute confidence intervals: alpha = 0.05; ci2 = spm1d.stats.ci_pairedsample(variableA, variableB, alpha, 'meanA', 'tailB'); disp(ci2)
%(2) Plot: close all figure('position', [0 0 1000 300]) myxlim = [0 size(variableA,2)-1];
% plot hypothesis test: subplot(122) spmi = spm1d.stats.ttest_paired(variableA, variableB).inference(alpha); spmi.plot(); spmi.plot_threshold_label(); spmi.plot_p_values(); xlabel('Normalised Movement Time (%)' ,'FontSize',10,'FontWeight','bold','Color','k');
%plot CI (possibility 3) subplot(121) ci2.plot() xlim(myxlim) title('Mean and 95% CI') xlabel('Normalised Movement Time (%)' ,'FontSize',10,'FontWeight','bold','Color','k'); ylabel('Velocity m/s)' ,'FontSize',10,'FontWeight','bold','Color','k');