Perform Inference on Algorithm-Agnostic Variable Importance

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Documentation for package ‘vimp’ version 2.3.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
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average_vim	Average multiple independent importance estimates
bootstrap_se	Compute bootstrap-based standard error estimates for variable importance
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 using Cross-fitting
estimate	Estimate a Predictiveness Measure
estimate.predictiveness_measure	Obtain a Point Estimate and Efficient Influence Function Estimate for a Given 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
est_predictiveness	Estimate a nonparametric predictiveness functional
est_predictiveness_cv	Estimate a nonparametric predictiveness functional using cross-fitting
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.SuperLearner
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
spvim_ics	Influence function estimates for SPVIMs
spvim_se	Standard error estimate for SPVIM values
sp_vim	Shapley Population Variable Importance Measure (SPVIM) Estimates and Inference
vim	Nonparametric Intrinsic Variable Importance Estimates and Inference
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_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 delta importance
vimp_regression	Nonparametric Intrinsic Variable Importance Estimates: ANOVA
vimp_rsquared	Nonparametric Intrinsic Variable Importance Estimates: R-squared
vimp_se	Estimate variable importance standard errors
vrc01	Neutralization sensitivity of HIV viruses to antibody VRC01