Fix yeojohnson lambda estimate ignoring missing values#28
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estimate_yeojohnson_lambda computed n with length(x) before dropping NAs, so the count of "observations" included missing values. That inflated n in the log-likelihood, shifting the optimized lambda. Appending NAs to a vector therefore changed the estimated lambda. Compute n after removing missing values so the log-likelihood uses the number of nonmissing observations. Add a regression test asserting the estimated lambda is unchanged when NAs are appended.
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Problem
estimate_yeojohnson_lambda()computed the observation count withn <- length(x)before dropping missing values, sonincluded theNAs. That inflatednfeeds the log-likelihood thatoptimize()maximizes:Because the inflated n reweights the first term, the optimized lambda shifts. The practical symptom: appending a few NAs to a vector changed the estimated lambda for the otherwise-identical data.
Fix
Moving
n <- length(x)to after dropping the missing values fixes the issue.Added a regression test as well.
Updated the version to 1.9.3 to reflect the bug fix.