Refactor Analyze Poisson using broom #114
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gwu05
reviewed
Feb 10, 2022
| dfAnalyzed$PredictedCount <- exp(dfAnalyzed$LogExposure*cModel$coefficients[2]+cModel$coefficients[1]) | ||
| dfAnalyzed$PValue = stats::pnorm( abs(dfAnalyzed$Residuals) , lower.tail=F ) * 2 | ||
| dfAnalyzed <- dfAnalyzed[order(abs(dfAnalyzed$Residuals) , decreasing=T), ] | ||
| dfAnalyzed <- broom::augment(cModel, dfModel, type.predict = "response") %>% |
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Probably something looked into a while back, but I forget the difference here between Residuals vs standardized residuals for the formula in the Poisson. I think we probably only need to keep the relevant columns (two residuals columns, and the additional hat (leverage), sigma (sd), cooksd (influence), may confuse user and probably can be hidden for now)
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Yeah. I agree. Will think about it a bit more once wilcoxon and maybe disposition are done and try to come up with some standardized naming.
gwu05
approved these changes
Feb 10, 2022
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Details
Refactoring Analyze Poisson using broom.
Risk Assessment
Risk: Medium
Mitigation Strategy:
Notes: Moderate risk since it's refactoring a statistical workflow, but changes are really mostly moving towards tidyverse syntax. Underlying model is still run by Poisson. Since we are pre-v1, will cover the full QC checklist separate from this PR. Filed #113 to tackle base unit tests in future PR.