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<div style="direction: ltr;font-family: Tahoma;color: #000000;font-size: 10pt;">Dear fieldtrippers,
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<div>I am trying to combine the Wilcoxon signrank test statistic with the cluster-based permutation approach. To this end, I wrote my own statfun, which calculates the Wilcoxon test statistic (using signrank.m in the stats toolbox) <span style="font-size: 13.3333px;">for
each random partition. The output of my statfun is s.stat, which contains </span><span style="font-size: 13.3333px;">a z-value (which is output by the function signrank.m for high enough N).</span><span style="font-size: 13.3333px;"> </span></div>
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<div><span style="font-size: 13.3333px;">I understand that the cluster-based test does not depend on the specific test statistic used. My question applies to the initial threshold, which is used for clustering. As I see it, I have three options here.</span></div>
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<div><span style="font-size: 13.3333px;">1) parametric </span></div>
<div><span style="font-size: 13.3333px;">In this case, I would calculate the critical value(s) for a normal distribution (+/- 1.96) and set this as s.critval.</span></div>
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<div><span style="font-size: 13.3333px;">2 - 3) nonparametric_individual / </span><span style="font-size: 13.3333px;">nonparametric_common</span></div>
<div><span style="font-size: 13.3333px;">I assume the threshold is here based on the permutation distribution with those samples chosen for clustering that exceed the 1-clusteralpha quantile. is this assumption correct? What is the difference between those
two methods? </span></div>
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<div>In general, <span style="font-size: 10pt;">I am unsure which of the three methods to choose. Which are the advantages / disadvantages of each option? </span><span style="font-size: 10pt;">I would be very happy about your feedback!</span></div>
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<div><span style="font-size: 10pt;">Best,</span></div>
<div>Andreas</div>
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