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I am graphing measured results versus expected results from a model, grouped by categories (the category in the boxplot below is one of a few different ones I'm using). For each data point, I subtracted the expected from the observed to determine the difference. My task is to modify the model to minimize the difference.

I would like to add the significance level to this chart but all resources I am finding are to compare means of each category to one another. In this case, I would like to know if each of the category's means is significantly different from 0. I can run this test one by one, selecting for data points falling within each category and testing for a difference from 0, but this seems inefficient.

Is there a way to automatically generate this and plot it? stat_compare_means seemed promising but I couldn't figure out how to make it work, while stat_pvalue_manual may hold more promise if I figure out how to code this.

Thanks in advance!

Sample boxplot (too new to add preview)

David Foster
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  • What is `stat_compare_means`? `stat_pvalue_manual `? They aren't ggplot2, and that plot looks like base graphics to me. It sounds like you need help with the analysis, but your tags and sample boxplot suggest you are just looking to "plot" things. This question is lacking any reproducibility, please read https://stackoverflow.com/q/5963269, [mcve], and https://stackoverflow.com/tags/r/info, then come back, [edit] your question, and add sample data (`dput(.)`), code attempted, and expand on your expected results. – r2evans Apr 27 '22 at 18:14

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