Calculate the expected conditional variance term

This function calculates the expected conditional variance term Var(Xj |X-j)

calculate.V_mean(
  S,
  algo = "lasso",
  cv.rule = "min",
  out = NULL,
  Xmodel = "gaussian",
  sigma_X.list_S = NULL,
  verbose = FALSE
)

Arguments

S

a list of selected variables.
algo

a fitting algorithm (default: "lasso").
cv.rule

indicates which rule should be used for the predict function, either "min" (the usual rule) or "1se" (the one-standard- error rule); default: "min"). See the glmnet help files for details.
out

the fitted model from train.fun.
Xmodel

model of the covaraites (default: "gaussian").
sigma_X.list_S

a list of length |S|, with each element being the variance of the conditional distribution.
verbose

whether to show intermediate progress (default: FALSE).

Value

A vector of length |S|, whose element is the expected conditional variance term Var(Xj |X-j).

References

Zhang L, Janson L (2020). “Floodgate: Inference for Model-Free Variable Importance.” arXiv preprint arXiv:2007.01283.

See also

Other methods: calulate.mu_Xk(), fg.inference.binary(), fg.inference(), fit.mu(), floodgate.binary(), floodgate(), inference_general()