vimp2.3.3 package

Perform Inference on Algorithm-Agnostic Variable Importance

average_vim

Average multiple independent importance estimates

bootstrap_se

Compute bootstrap-based standard error estimates for variable importan...

check_fitted_values

Check pre-computed fitted values for call to vim, cv_vim, or sp_vim

check_inputs

Check inputs to a call to vim, cv_vim, or sp_vim

create_z

Create complete-case outcome, weights, and Z

cv_vim

Nonparametric Intrinsic Variable Importance Estimates and Inference us...

est_predictiveness

Estimate a nonparametric predictiveness functional

est_predictiveness_cv

Estimate a nonparametric predictiveness functional using cross-fitting

estimate.predictiveness_measure

Obtain a Point Estimate and Efficient Influence Function Estimate for ...

estimate

Estimate a Predictiveness Measure

estimate_eif_projection

Estimate projection of EIF on fully-observed variables

estimate_nuisances

Estimate nuisance functions for average value-based VIMs

estimate_type_predictiveness

Estimate Predictiveness Given a Type

extract_sampled_split_predictions

Extract sampled-split predictions from a CV.SuperLearner object

format.predictiveness_measure

Format a predictiveness_measure object

format.vim

Format a vim object

get_cv_sl_folds

Get a numeric vector with cross-validation fold IDs from CV.SuperLearn...

get_full_type

Obtain the type of VIM to estimate using partial matching

get_test_set

Return test-set only data

make_folds

Create Folds for Cross-Fitting

make_kfold

Turn folds from 2K-fold cross-fitting into individual K-fold folds

measure_accuracy

Estimate the classification accuracy

measure_anova

Estimate ANOVA decomposition-based variable importance.

measure_auc

Estimate area under the receiver operating characteristic curve (AUC)

measure_average_value

Estimate the average value under the optimal treatment rule

measure_cross_entropy

Estimate the cross-entropy

measure_deviance

Estimate the deviance

measure_mse

Estimate mean squared error

measure_r_squared

Estimate R-squared

merge_vim

Merge multiple vim objects into one

predictiveness_measure

Construct a Predictiveness Measure

print.predictiveness_measure

Print predictiveness_measure objects

print.vim

Print vim objects

process_arg_lst

Process argument list for Super Learner estimation of the EIF

run_sl

Run a Super Learner for the provided subset of features

sample_subsets

Create necessary objects for SPVIMs

scale_est

Return an estimator on a different scale

sp_vim

Shapley Population Variable Importance Measure (SPVIM) Estimates and I...

spvim_ics

Influence function estimates for SPVIMs

spvim_se

Standard error estimate for SPVIM values

vim

Nonparametric Intrinsic Variable Importance Estimates and Inference

vimp

vimp: Perform Inference on Algorithm-Agnostic Intrinsic Variable Impor...

vimp_accuracy

Nonparametric Intrinsic Variable Importance Estimates: Classification ...

vimp_anova

Nonparametric Intrinsic Variable Importance Estimates: ANOVA

vimp_auc

Nonparametric Intrinsic Variable Importance Estimates: AUC

vimp_ci

Confidence intervals for variable importance

vimp_deviance

Nonparametric Intrinsic Variable Importance Estimates: Deviance

vimp_hypothesis_test

Perform a hypothesis test against the null hypothesis of δ\delta impo...

vimp_regression

Nonparametric Intrinsic Variable Importance Estimates: ANOVA

vimp_rsquared

Nonparametric Intrinsic Variable Importance Estimates: R-squared

vimp_se

Estimate variable importance standard errors

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

  • Maintainer: Brian D. Williamson
  • License: MIT + file LICENSE
  • Last published: 2023-08-28