Package: vimp 2.3.3

vimp: Perform Inference on Algorithm-Agnostic Variable Importance

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).

Authors:Brian D. Williamson [aut, cre], Jean Feng [ctb], Charlie Wolock [ctb], Noah Simon [ths], Marco Carone [ths]

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vimp/json (API)
NEWS

# Install 'vimp' in R:
install.packages('vimp', repos = c('https://bdwilliamson.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/bdwilliamson/vimp/issues

Datasets:
  • vrc01 - Neutralization sensitivity of HIV viruses to antibody VRC01

On CRAN:

machine-learningnonparametric-statisticsstatistical-inferencevariable-importance

43 exports 21 stars 2.36 score 32 dependencies 1 mentions 61 scripts 429 downloads

Last updated 9 months agofrom:b5e8382560. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 09 2024
R-4.5-winNOTESep 09 2024
R-4.5-linuxNOTESep 09 2024
R-4.4-winNOTESep 09 2024
R-4.4-macNOTESep 09 2024
R-4.3-winNOTESep 09 2024
R-4.3-macNOTESep 09 2024

Exports:average_vimbootstrap_secheck_fitted_valuescheck_inputscreate_zcv_vimest_predictivenessest_predictiveness_cvestimateestimate_nuisancesextract_sampled_split_predictionsget_cv_sl_foldsget_full_typeget_test_setmake_foldsmake_kfoldmeasure_accuracymeasure_anovameasure_aucmeasure_average_valuemeasure_cross_entropymeasure_deviancemeasure_msemeasure_r_squaredmerge_vimpredictiveness_measureprocess_arg_lstrun_slsample_subsetsscale_estsp_vimspvim_icsspvim_sevimvimp_accuracyvimp_anovavimp_aucvimp_civimp_deviancevimp_hypothesis_testvimp_regressionvimp_rsquaredvimp_se

Dependencies:bitopsbootcaToolsclicodetoolscvAUCdata.tabledplyrfansiforeachgamgenericsgluegplotsgtoolsiteratorsKernSmoothlifecyclemagrittrMASSnnlspillarpkgconfigR6rlangROCRSuperLearnertibbletidyselectutf8vctrswithr

Introduction to vimp

Brian D. Williamson

Rendered fromintroduction-to-vimp.Rmdusingknitr::rmarkdownon Sep 09 2024.

Last update: 2022-12-09
Started: 2020-06-23

Types of VIMs

Brian D. Williamson

Rendered fromtypes-of-vims.Rmdusingknitr::rmarkdownon Sep 09 2024.

Last update: 2021-08-03
Started: 2020-06-23

Using precomputed regression function estimates in vimp

Brian D. Williamson

Rendered fromprecomputed-regressions.Rmdusingknitr::rmarkdownon Sep 09 2024.

Last update: 2023-12-14
Started: 2020-06-23

Variable importance with coarsened data

Brian D. Williamson

Rendered fromipcw-vim.Rmdusingknitr::rmarkdownon Sep 09 2024.

Last update: 2022-07-26
Started: 2022-03-31

Readme and manuals

Help Manual

Help pageTopics
Average multiple independent importance estimatesaverage_vim
Compute bootstrap-based standard error estimates for variable importancebootstrap_se
Check pre-computed fitted values for call to vim, cv_vim, or sp_vimcheck_fitted_values
Check inputs to a call to vim, cv_vim, or sp_vimcheck_inputs
Create complete-case outcome, weights, and Zcreate_z
Nonparametric Intrinsic Variable Importance Estimates and Inference using Cross-fittingcv_vim
Estimate a nonparametric predictiveness functionalest_predictiveness
Estimate a nonparametric predictiveness functional using cross-fittingest_predictiveness_cv
Estimate a Predictiveness Measureestimate
Estimate projection of EIF on fully-observed variablesestimate_eif_projection
Estimate nuisance functions for average value-based VIMsestimate_nuisances
Estimate Predictiveness Given a Typeestimate_type_predictiveness
Obtain a Point Estimate and Efficient Influence Function Estimate for a Given Predictiveness Measureestimate.predictiveness_measure
Extract sampled-split predictions from a CV.SuperLearner objectextract_sampled_split_predictions
Format a 'predictiveness_measure' objectformat.predictiveness_measure
Format a 'vim' objectformat.vim
Get a numeric vector with cross-validation fold IDs from CV.SuperLearnerget_cv_sl_folds
Obtain the type of VIM to estimate using partial matchingget_full_type
Return test-set only dataget_test_set
Create Folds for Cross-Fittingmake_folds
Turn folds from 2K-fold cross-fitting into individual K-fold foldsmake_kfold
Estimate the classification accuracymeasure_accuracy
Estimate ANOVA decomposition-based variable importance.measure_anova
Estimate area under the receiver operating characteristic curve (AUC)measure_auc
Estimate the average value under the optimal treatment rulemeasure_average_value
Estimate the cross-entropymeasure_cross_entropy
Estimate the deviancemeasure_deviance
Estimate mean squared errormeasure_mse
Estimate R-squaredmeasure_r_squared
Merge multiple 'vim' objects into onemerge_vim
Construct a Predictiveness Measurepredictiveness_measure
Print 'predictiveness_measure' objectsprint.predictiveness_measure
Print 'vim' objectsprint.vim
Process argument list for Super Learner estimation of the EIFprocess_arg_lst
Run a Super Learner for the provided subset of featuresrun_sl
Create necessary objects for SPVIMssample_subsets
Return an estimator on a different scalescale_est
Shapley Population Variable Importance Measure (SPVIM) Estimates and Inferencesp_vim
Influence function estimates for SPVIMsspvim_ics
Standard error estimate for SPVIM valuesspvim_se
Nonparametric Intrinsic Variable Importance Estimates and Inferencevim
Nonparametric Intrinsic Variable Importance Estimates: Classification accuracyvimp_accuracy
Nonparametric Intrinsic Variable Importance Estimates: ANOVAvimp_anova
Nonparametric Intrinsic Variable Importance Estimates: AUCvimp_auc
Confidence intervals for variable importancevimp_ci
Nonparametric Intrinsic Variable Importance Estimates: Deviancevimp_deviance
Perform a hypothesis test against the null hypothesis of delta importancevimp_hypothesis_test
Nonparametric Intrinsic Variable Importance Estimates: ANOVAvimp_regression
Nonparametric Intrinsic Variable Importance Estimates: R-squaredvimp_rsquared
Estimate variable importance standard errorsvimp_se
Neutralization sensitivity of HIV viruses to antibody VRC01vrc01