merge_vim: Merge multiple 'vim' objects into one

In bdwilliamson/nova: Perform Inference on Algorithm-Agnostic Variable Importance

Merge multiple vim objects into one

Description

Take the output from multiple different calls to vimp_regression and merge into a single vim object; mostly used for plotting results.

Usage

merge_vim(...)

Arguments

...	an arbitrary number of vim objects, separated by commas.

Value

an object of class vim containing all of the output from the individual vim objects. This results in a list containing:

• s - a list of the column(s) to calculate variable importance for

• SL.library - a list of the libraries of learners passed to SuperLearner

• full_fit - a list of the fitted values of the chosen method fit to the full data

• red_fit - a list of the fitted values of the chosen method fit to the reduced data

• est- a vector with the corrected estimates

• naive- a vector with the naive estimates

• eif- a list with the influence curve-based updates

• se- a vector with the standard errors

• ci- a matrix with the CIs

• mat - a tibble with the estimated variable importance, the standard errors, and the (1-α) \times 100% confidence intervals

• full_mod - a list of the objects returned by the estimation procedure for the full data regression (if applicable)

• red_mod - a list of the objects returned by the estimation procedure for the reduced data regression (if applicable)

• alpha - a list of the levels, for confidence interval calculation

Examples

# generate the data
# generate X
p <- 2
n <- 100
x <- data.frame(replicate(p, stats::runif(n, -5, 5)))

# apply the function to the x's
smooth <- (x[,1]/5)^2*(x[,1]+7)/5 + (x[,2]/3)^2

# generate Y ~ Normal (smooth, 1)
y <- smooth + stats::rnorm(n, 0, 1)

# set up a library for SuperLearner; note simple library for speed
library("SuperLearner")
learners <- c("SL.glm", "SL.mean")

# using Super Learner (with a small number of folds, for illustration only)
est_2 <- vimp_regression(Y = y, X = x, indx = 2, V = 2,
           run_regression = TRUE, alpha = 0.05,
           SL.library = learners, cvControl = list(V = 2))

est_1 <- vimp_regression(Y = y, X = x, indx = 1, V = 2,
           run_regression = TRUE, alpha = 0.05,
           SL.library = learners, cvControl = list(V = 2))

ests <- merge_vim(est_1, est_2)

bdwilliamson/nova documentation built on Feb. 13, 2023, 9:52 a.m.