330 consecutive imputation links #15
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tests/cumulative_links.csv
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| 100,100000,,202404,3,1,2,6,1 | ||
| 200,100001,,202402,1,4,3,1,2 | ||
| 200,100001,,202403,3,0.5,3,3,0.5 | ||
| 200,100001,300,202404,0.5,1,4,0.5,1 |
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I might be misunderstanding the code / method, but why isn't cumulative_forward_imputation_link for the last row 1.5. Is that because it is in a different imputation group?
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yes that is correct. We only want to multiply the links where there are consecutive missing values. In fact, maybe we should turn all cumulative links in the same row as a return to nan because it has no meaning.
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to confirm, the last row is just the same as the imputation_link column because it is a return not a missing value. I have made a change to turn the cumulative link columns to NaN if there is a return
| 100,100000,,202404,3,1,2,6,1 | ||
| 200,100001,,202402,1,4,3,1,2 | ||
| 200,100001,,202403,3,0.5,3,3,0.5 | ||
| 200,100001,300,202404,0.5,1,4,, |
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I think this makes more sense now that the cumulative link doesn't return anything in cases where there is a valid return :)
Summary
Multiple consecutive missing values need imputation links to be multiplied to apply the correct imputation link. This function creates two new columns onto the input DataFrame, one for imputation_group that defines consecutive missing values, and another column for cumulative links.
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