Refuse an all-NA additional covariate up front - #77
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correspondMerchant merged 1 commit intoAug 10, 2026
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An additional covariate whose column is entirely NA made glm()'s internal na.omit drop every row, so the fit died with the opaque error "Error occurred during model fitting step: ... Argument mu must be a nonempty numeric vector", which never named the covariate. Unlike a collinear covariate, which glm() can drop and still fit (and which already warns), an all-NA covariate is always a mistake and can never enter the model. validate_inputs() now refuses it up front, after the column-existence check, naming the offending covariate(s) in one error. all(is.na(col)) catches an all-NA column of any type (numeric, factor, character). No previously successful run changes; the check is narrow (a partially-observed covariate is untouched). Adds end-to-end and unit tests: the refusal fires and names the covariate, catches all-NA factor/character columns, and stays silent on some-NA and fully-observed covariates. The end-to-end test asserts the clear message replaces the cryptic model-fitting error and fails on the prior source.
correspondMerchant
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August 10, 2026 17:50
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An additional covariate whose column is entirely NA made glm()'s internal na.omit drop every row, so the fit died with the opaque error "Error occurred during model fitting step: ... Argument mu must be a nonempty numeric vector", which never named the covariate. Unlike a collinear covariate, which glm() can drop and still fit (and which already warns), an all-NA covariate is always a mistake and can never enter the model.
validate_inputs() now refuses it up front, after the column-existence check, naming the offending covariate(s) in one error. all(is.na(col)) catches an all-NA column of any type (numeric, factor, character). No previously successful run changes; the check is narrow (a partially-observed covariate is untouched).
Adds end-to-end and unit tests: the refusal fires and names the covariate, catches all-NA factor/character columns, and stays silent on some-NA and fully-observed covariates. The end-to-end test asserts the clear message replaces the cryptic model-fitting error and fails on the prior source.