![]() We mightĪlso want to be model-agnostic and use a more nonparametric approach to We mightĬhoose a classical parametric tool such as linear regression. There are many tools for estimating this conditional mean. Often when working with data we attempt to estimate the conditionalįeatures \(X\), defined as \(\mu_P(x) = E_P(Y \mid X = x)\). Study, see Magaret, Benkeser, Williamson, et al. Predicting the neutralization sensitivity of the HIV virus to theīroadly neutralizing antibody VRC01. In this section, we provide a fuller example of estimating \(R^2\)-based variable importance in theĬontext of assessing the importance of amino acid sequence features in This second-to-last argument, SL.library, determines theĮstimators you want to use for the conditional mean of Y given X.Įstimates of variable importance rely on good estimators of theĬonditional mean, so we suggest using flexible estimators and model V: the number of folds to use for cross-fitted variable. ![]() SL.library: a “library” of learners to pass to the function.Whether or not to run a regression of Y on X ( TRUE in this run_regression: a logical value telling vimp_rsquared.indx: the covariate(s) of interest for evaluating importance (here,.Regressions yourself and plug these into vimp. Regression function estimates in vimp”), you run the Regressions for you and return variable importance in the second method Importance: in the first method, you allow vimp to run The workhorse function of vimp, for \(R^2\)-based variable importance, is Set of folds for a sample of n = 100 study This creates a matrix of covariates x with two columns,Ī vector y of normally-distributed outcome values, and a # - # problem setup # - # set up the data set.seed( 5678910) n <- 1000 p <- 2 s <- 1 # desire importance for X_1 x <- ame( replicate(p, runif(n, - 1, 1))) y <- (x) ^ 2 *(x + 7 / 5) + ( 25 / 9) *(x) ^ 2 + rnorm(n, 0, 1) # set up folds for hypothesis testing folds <- sample( rep( seq_len( 2), length = length(y))) Implemented here have also been implemented in Python under the package The author and maintainer of the vimp package is Brian Williamson. To estimate the importance of any single feature or group of featuresįor predicting the outcome. The code can handle arbitrary dimensions of features, and may be used These techniques are slow, then the variable importance procedure will The techniques used to estimate the underlying conditional means - if Variable importance estimates may be computed quickly, depending on The accompanying manuscripts Williamson, Gilbert, In simple parametric models (e.g., linear models). Quantities are all nonparametric generalizations of the usual measures Under the receiver operating characteristic curve (AUC). Importance based on the difference in nonparametric \(R^2\), classification accuracy, and area The package supports flexible estimation of variable Of variable importance and provides valid inference on the true Vimp is a package that computes nonparametric estimates
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