A Statistically Sound 'data.frame' Processor/Conditioner


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Documentation for package ‘vtreat’ version 1.3.7

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vtreat-package vtreat: A Statistically Sound 'data.frame' Processor/Conditioner
as_rquery_plan Convert vtreatment plans into a sequence of rquery operations.
buildEvalSets Build set carve-up for out-of sample evaluation.
center_scale Center and scale a set of variables.
designTreatmentsC Build all treatments for a data frame to predict a categorical outcome.
designTreatmentsN build all treatments for a data frame to predict a numeric outcome
designTreatmentsZ Design variable treatments with no outcome variable.
design_missingness_treatment Design a simple treatment plan to indicate missingingness and perform simple imputation.
format.vtreatment Display treatment plan.
getSplitPlanAppLabels read application labels off a split plan.
kWayCrossValidation k-fold cross validation, a splitFunction in the sense of vtreat::buildEvalSets
kWayStratifiedY k-fold cross validation stratified on y, a splitFunction in the sense of vtreat::buildEvalSets
kWayStratifiedYReplace k-fold cross validation stratified with replacement on y, a splitFunction in the sense of vtreat::buildEvalSets .
makekWayCrossValidationGroupedByColumn Build a k-fold cross validation splitter, respecting (never splitting) groupingColumn.
materialize_treated Materialize a treated data frame remotely.
mkCrossFrameCExperiment Run categorical cross-frame experiment.
mkCrossFrameMExperiment Function to build multi-outcome vtreat cross frame and treatment plan.
mkCrossFrameNExperiment Run a numeric cross frame experiment.
novel_value_summary Report new/novel appearances of character values.
oneWayHoldout One way holdout, a splitFunction in the sense of vtreat::buildEvalSets.
patch_columns_into_frame Patch columns into data.frame.
ppCoderC Solve a categorical partial pooling problem.
ppCoderN Solve a numeric partial pooling problem.
prepare Apply treatments and restrict to useful variables.
prepare.multinomial_plan Function to apply mkCrossFrameMExperiment treatemnts.
prepare.simple_plan Prepare a simple treatment.
prepare.treatmentplan Apply treatments and restrict to useful variables.
pre_comp_xval Pre-computed cross-plan (so same split happens each time).
print.multinomial_plan Print treatmentplan.
print.simple_plan Print treatmentplan.
print.treatmentplan Print treatmentplan.
print.vtreatment Print treatmentplan.
problemAppPlan check if appPlan is a good carve-up of 1:nRows into nSplits groups
rquery_prepare Materialize a treated data frame remotely.
run_vtreat_tests Run vtreat tests.
solveIsotone Solve for best single-direction (non-decreasing or non-increasing) fit.
solveNonDecreasing Solve for best non-decreasing fit using isotone regression (from the "isotone" package <URL: https://CRAN.R-project.org/package=isotone>).
solveNonIncreasing Solve for best non-increasing fit.
solve_piecewise Solve as piecewise linear problem, numeric target.
solve_piecewisec Solve as piecewise logit problem, categorical target.
spline_variable Spline variable numeric target.
spline_variablec Spline variable categorical target.
square_window Build a square windows variable, numeric target.
square_windowc Build a square windows variable, categorical target.
track_values Track unique character values for variables.
value_variables_C Value variables for prediction a categorical outcome.
value_variables_N Value variables for prediction a numeric outcome.
variable_values Return variable evaluations.
vnames New treated variable names from a treatmentplan$treatment item.
vorig Original variable name from a treatmentplan$treatment item.
vtreat vtreat: A Statistically Sound 'data.frame' Processor/Conditioner