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What's the best way to determine if a particular host plant species (host_plant column) was infected in significantly higher number by a certain pathogen genus (pahothogen_genus column)? I have collected disease reports for three plant disease (rust, powdery mildew, downy mildew) for the past decade (2010-2020), using a systematic review. I would like to know if a particular plant species was getting infected more by a particular pathogen genus. Ultimately, I want to determine if the pathogen is generalist if there is no significant relationship with a certain host plant. A link to an example analyses would be awesome. Thanks. Here is the glimpse of my data

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One approach would be to use a permutation test. Use your real data to calculate a specificity metric - perhaps the proportion of occurrences of your pathogen in one plant species. Then randomise your data, and re-calculate the metric, repeating many times (e.g. 1000) to generate a distribution of expected metrics. You can then test whether your observed metric falls outside the 95% expected distribution.

This is a fairly standard approach to testing for specificity/generalism, but you must carefully consider whether your randomisations make sense - what is randomised, and how is of paramount importance for the test to be valid.

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  • $\begingroup$ Hi, Thanks. Could you please direct me to an example analyses in R programming language? $\endgroup$
    – Ahsk
    Jan 11, 2022 at 5:10
  • $\begingroup$ Not really - the nature of permutation tests mean they need to be tailored to your particular hypothesis. The calculation is usually fairly simple - the tricky thing is defining a sensible metric and null distribution. This R-Bloggers page talks about them: r-bloggers.com/2019/04/what-is-a-permutation-test Here's a page with some example code: towardsdatascience.com/permutation-test-in-r-77d551a9f891 You can probably find more just by googling "permutation test example R" $\endgroup$
    – rw2
    Jan 11, 2022 at 9:24

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