impute_block: Constructs an overall matrix, then applies a given imputation...

In dcgerard/vicar: Various Ideas for Confounder Adjustment in Regression

Description

Constructs an overall matrix, then applies a given imputation function.

Usage

impute_block(
  Y21,
  Y31,
  Y32,
  impute_func,
  impute_args = list(),
  do_variance = FALSE
)

Arguments

Y21	A matrix of size k by m
Y31	A matrix of size (n - k) by m
Y32	A matrix of size (n - k) by (p - m)
impute_func	A function that takes as input a matrix names Y that has missing values and returns a matrix called Yhat of the same dimension of Y with the missing values filled in. If do_variance = TRUE, then impute_func should also return sig_diag — a vector of column-specific variance estimates. The default is a wrapper for softImpute. I provide a few functions in this package. knn_wrapper performs k-nearest-neighbors imputation. missforest_wrapper performs random forest imputation. softimpute_wrapper performs nuclear norm minimization imputation.
impute_args	A list of additional parameters to pass to impute_func.
do_variance	A logical. Does impute_func also return estimates of the column-specific variances?

Value

Y22 A matrix of size k by (p - m). This is the imputed submatrix.

sig_diag If do_variance = FALSE, then this is NULL. Else, it is a vector of column-specific variance estimates.

Author(s)

David Gerard

dcgerard/vicar documentation built on July 7, 2021, 1:08 p.m.