Customizing the rpart library for multivariate gaussian outcomes: the longRPart library
Bibliographic record
Abstract
Abdolell et al. (2001) implemented a binary partitioning algorithm for the case of continuous repeated measures outcomes, using Mahalanobis distance as a deviance measure to evaluate goodness-of-split. The algorithm was implemented only for a single split at the root node of the tree with the single purpose of dichotomising prognostic variables. The binary partitioning algorithm was implemented in SAS using the PROC MIXED procedure, along with a permutation test to evaluate the p-value of the associated binary split and a bootstrap method to calculate a confidence interval. This project extends the binary partitioning algorithm of Abdolell et. al to a binary recursive partitioning algorithm which is implemented in R. We utilize the nlme library to extend the rpart library, producing the longRPart library for binary recursive partitioning in the case of MVN outcomes, and extends the algorithm to split on unordered categorical variables. A tree plotting function is developed for annotated plots that are applied to terminal nodes of the tree to display the longitudinal profiles of the outcome variable. A detailed discussion will be presented of how the rpart library was extended to accomodate the longitudinal outcome with its associated deviance measure, and how to apply these same principles to the case of other non-standard outcomes using custom R functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".