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 |