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# Mantel tests on Network analyis matrices

By Joana Martinho Wilson | Jan 27, 2017 05:44PM CET

I'm running a network analysis on telemetry data, so I have matrices on the movements of the animals between few locations (telemetry receivers) during especific environmental conditions, one matrix for each condition (e.g. day/night). Can I compare that matrices in order to detect differences in movement patterns according to environmental conditions with Mantel test? Do input matrices have to be similarity/dissimilarity type? Can I apply the mantel test directly on the movement matrices? The interpretation of the p-value is similar to any other mantel test?

By Sébastien | Jan 30, 2017 11:43AM CET | XLSTAT Agent

Hi,

Thanks for your question. The Mantel test works on symmetrical matrices, which means rows and columns contain the same objects, and the number linking object a to object b is the same as the number linking object b to object a. This is the case of Similarity / Dissimilarity matrices that are used most of the time in this test. Objects should also be the same between the two matrices for which you are running the test. Technically, you can run the test as soon as your matrices meet these conditions. The p-value is linked to the null hypothesis of the absence of any correlation between the two matrices.

I hope this helps.

Jean Paul

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By Sébastien | Jan 30, 2017 11:43AM CET | XLSTAT Agent

Hi,

Thanks for your question. The Mantel test works on symmetrical matrices, which means rows and columns contain the same objects, and the number linking object a to object b is the same as the number linking object b to object a. This is the case of Similarity / Dissimilarity matrices that are used most of the time in this test. Objects should also be the same between the two matrices for which you are running the test. Technically, you can run the test as soon as your matrices meet these conditions. The p-value is linked to the null hypothesis of the absence of any correlation between the two matrices.

I hope this helps.

Jean Paul

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