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Only displaying tests that are significant #110
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I was going to ask for a similar feature. I would like to filter p-values before we know the answer. In my case, I want to show only NS, which I cannot know prior to run the code and it is hard work to select pairs one-by-one afterwards. I would like to automate this process. |
It would be welcome to have both features indeed, with an optional parameter which, by default, would not filter anything. I don't have enough knowledge of the package to dive into the code unfortunately. |
+1 This might be a workaround to annotate only the significant p-values BUT it affects the p-values in multiple test corrections. # create annotator
annotator = Annotator(axs, pairs, data=df, x=x, y=y, order=order)
# configure
annotator.configure(test=test, comparisons_correction=corr, text_format='star', loc='inside')
# apply ONLY tests
annotator.apply_test()
# pick the pairs producing significant p-value
sig_pairs = [annotator.pairs[i] for i, x in enumerate(annotator.get_annotations_text()) if x != "ns"]
# decide what to do
if len(sig_pairs) == len(pairs): # all pairs gave significant p-values -> annotate
annotator.annotate()
elif len(sig_pairs) == 0: # there are no significant pairs so don't add anything to the plot
pass
else: # some pairs were not significant -> use only those
annotator = Annotator(axs, sig_pairs, data=df, x=x, y=y, order=order)
annotator.configure(test=test, comparisons_correction=corr, text_format='star', loc='inside')
annotator.apply_and_annotate() |
For hiding non significant results, we merged #95 into our |
Merged today and released in v0.6.0! |
The tests that are displayed are defined by what's fed into the argument
pairs
ofAnnotator
but we don't know prior to runningapply_and_annotate
what tests are significant. I don't think there's an easy way to eliminate from the list fed topairs
thepairs
that are not significant.The text was updated successfully, but these errors were encountered: