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vimp (version 2.2.5)

Perform Inference on Algorithm-Agnostic Variable Importance

Description

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. (arXiv, 2020+) , and Williamson and Feng (ICML, 2020).

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Install

install.packages('vimp')

Monthly Downloads

273

Version

2.2.5

License

MIT + file LICENSE

Issues

0

Pull Requests

0

Stars

24

Forks

8

Maintainer

Brian D. Williamson

Last Published

August 16th, 2021

Functions in vimp (2.2.5)

get_cv_sl_folds

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

Create Folds for Cross-Fitting
bootstrap_se

Compute bootstrap-based standard error estimates for variable importance
make_kfold

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

Nonparametric Intrinsic Variable Importance Estimates and Inference
spvim_se

Standard error estimate for SPVIM values
sample_subsets

Create necessary objects for SPVIMs
get_full_type

Obtain the type of VIM to estimate using partial matching
scale_est

Return an estimator on a different scale
measure_anova

Estimate ANOVA decomposition-based variable importance.
sp_vim

Shapley Population Variable Importance Measure (SPVIM) Estimates and Inference
measure_accuracy

Estimate the classification accuracy
measure_deviance

Estimate the deviance
measure_mse

Estimate mean squared error
measure_auc

Estimate area under the receiver operating characteristic curve (AUC)
measure_cross_entropy

Estimate the cross-entropy
measure_r_squared

Estimate R-squared
vimp_hypothesis_test

Perform a hypothesis test against the null hypothesis of \(\delta\) importance
spvim_ics

Influence function estimates for SPVIMs
vimp_regression

Nonparametric Intrinsic Variable Importance Estimates: ANOVA
vimp_se

Estimate variable importance standard errors
vimp_rsquared

Nonparametric Intrinsic Variable Importance Estimates: R-squared
vimp_anova

Nonparametric Intrinsic Variable Importance Estimates: ANOVA
vimp_auc

Nonparametric Intrinsic Variable Importance Estimates: AUC
run_sl

Run a Super Learner for the provided subset of features
vimp_accuracy

Nonparametric Intrinsic Variable Importance Estimates: Classification accuracy
vimp

vimp: Perform Inference on Algorithm-Agnostic Intrinsic Variable Importance
print.vim

Print a vim object
vrc01

Neutralization sensitivity of HIV viruses to antibody VRC01
vimp_ci

Confidence intervals for variable importance
merge_vim

Merge multiple vim objects into one
vimp_deviance

Nonparametric Intrinsic Variable Importance Estimates: Deviance
extract_sampled_split_predictions

Extract sampled-split predictions from a CV.SuperLearner object
check_fitted_values

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

Estimate a nonparametric predictiveness functional
check_inputs

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

Average multiple independent importance estimates
create_z

Create complete-case outcome, weights, and Z
format.vim

Format a vim object
est_predictiveness_cv

Estimate a nonparametric predictiveness functional using cross-fitting
cv_vim

Nonparametric Intrinsic Variable Importance Estimates and Inference using Cross-fitting