Function reference • vimp

Estimate specific variable importance parameters Functions that set default values to measure variable importance via specific parameters.
`vimp_accuracy()`	Nonparametric Intrinsic Variable Importance Estimates: Classification accuracy
`vimp_anova()`	Nonparametric Intrinsic Variable Importance Estimates: ANOVA
`vimp_auc()`	Nonparametric Intrinsic Variable Importance Estimates: AUC
`vimp_deviance()`	Nonparametric Intrinsic Variable Importance Estimates: Deviance
`vimp_rsquared()`	Nonparametric Intrinsic Variable Importance Estimates: R-squared
Estimate variable importance General functions to measure population variable importance and compute point estimates and confidence intervals.
`vim()`	Nonparametric Intrinsic Variable Importance Estimates and Inference
`cv_vim()`	Nonparametric Intrinsic Variable Importance Estimates and Inference using Cross-fitting
`vimp_se()`	Estimate variable importance standard errors
`vimp_ci()`	Confidence intervals for variable importance
`vimp_hypothesis_test()`	Perform a hypothesis test against the null hypothesis of \(\delta\) importance
`sp_vim()`	Shapley Population Variable Importance Measure (SPVIM) Estimates and Inference
`spvim_ics()`	Influence function estimates for SPVIMs
`spvim_se()`	Standard error estimate for SPVIM values
`sample_subsets()`	Create necessary objects for SPVIMs
`measure_anova()`	Estimate ANOVA decomposition-based variable importance.
`bootstrap_se()`	Compute bootstrap-based standard error estimates for variable importance
Predictiveness measures Functions to measure population predictiveness (e.g., R-squared or AUC) and compute point estimates and confidence intervals for predictiveness.
`predictiveness_measure()`	Construct a Predictiveness Measure
`estimate()`	Estimate a Predictiveness Measure
`estimate(<predictiveness_measure>)`	Obtain a Point Estimate and Efficient Influence Function Estimate for a Given Predictiveness Measure
`estimate_type_predictiveness()`	Estimate Predictiveness Given a Type
`measure_accuracy()`	Estimate the classification accuracy
`measure_average_value()`	Estimate the average value under the optimal treatment rule
`measure_auc()`	Estimate area under the receiver operating characteristic curve (AUC)
`measure_cross_entropy()`	Estimate the cross-entropy
`measure_deviance()`	Estimate the deviance
`measure_mse()`	Estimate mean squared error
`measure_r_squared()`	Estimate R-squared
`est_predictiveness_cv()`	Estimate a nonparametric predictiveness functional using cross-fitting
`est_predictiveness()`	Estimate a nonparametric predictiveness functional
Aggregate variable importance estimates Functions to aggregate individual variable importance objects (created with calls to `vim` or `cv_vim`) by averaging or merging into larger objects.
`average_vim()`	Average multiple independent importance estimates
`merge_vim()`	Merge multiple `vim` objects into one
Internal package utility functions Functions for internal convenience, used by other core functions.
`format(<predictiveness_measure>)`	Format a `predictiveness_measure` object
`print(<predictiveness_measure>)`	Print `predictiveness_measure` objects
`print(<vim>)`	Print `vim` objects
`format(<vim>)`	Format a `vim` object
`make_folds()`	Create Folds for Cross-Fitting
`create_z()`	Create complete-case outcome, weights, and Z
`make_kfold()`	Turn folds from 2K-fold cross-fitting into individual K-fold folds
`run_sl()`	Run a Super Learner for the provided subset of features
`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
`extract_sampled_split_predictions()`	Extract sampled-split predictions from a CV.SuperLearner object
`get_cv_sl_folds()`	Get a numeric vector with cross-validation fold IDs from CV.SuperLearner
`get_full_type()`	Obtain the type of VIM to estimate using partial matching
`scale_est()`	Return an estimator on a different scale
`process_arg_lst()`	Process argument list for Super Learner estimation of the EIF
`estimate_eif_projection()`	Estimate projection of EIF on fully-observed variables
`estimate_nuisances()`	Estimate nuisance functions for average value-based VIMs
`get_test_set()`	Return test-set only data
Deprecated functions Functions that are kept for backwards compatability; we recommend using the updated or replacement functions instead.
`vimp_regression()`	Nonparametric Intrinsic Variable Importance Estimates: ANOVA
Package documentation homepage Landing page for R help
`vimp` `vimp-package`	vimp: Perform Inference on Algorithm-Agnostic Intrinsic Variable Importance
VRC01 data Dataset for use in the vignette examples
`vrc01`	Neutralization sensitivity of HIV viruses to antibody VRC01

Reference

Estimate specific variable importance parameters

Estimate variable importance

Predictiveness measures

Aggregate variable importance estimates

Internal package utility functions

Deprecated functions

Package documentation homepage

VRC01 data