ruvem: Same as ruvimpute but only does ruvem and comes up with...
In dcgerard/vicar: Various Ideas for Confounder Adjustment in Regression

This is more friendly than ruvimpute.

ruvem(
  Y,
  X,
  ctl,
  k = NULL,
  impute_args = list(),
  cov_of_interest = ncol(X),
  include_intercept = TRUE,
  gls = TRUE
)

`Y`	A matrix of numerics. These are the response variables where each column has its own variance. In a gene expression study, the rows are the individuals and the columns are the genes.
`X`	A matrix of numerics. The covariates of interest.
`ctl`	A vector of logicals of length `ncol(Y)`. If position i is `TRUE` then position i is considered a negative control.
`k`	The rank of the underlying matrix. Used by `hard_impute` if that is the value of `impute_func`. If not provided, will be estimated by `num.sv`.
`impute_args`	A list of additional parameters to pass to `impute_func`.
`cov_of_interest`	A vector of positive integers. The column numbers of the covariates in X whose coefficients you are interested in. The rest are considered nuisance parameters and are regressed out by OLS.
`include_intercept`	A logical. If `TRUE`, then it will check `X` to see if it has an intercept term. If not, then it will add an intercept term. If `FALSE`, then `X` will be unchanged.
`gls`	A logical. Should we use GLS (`TRUE`) or OLS (`FALSE`)?

David Gerard

Gerard, David, and Matthew Stephens. 2021. "Unifying and Generalizing Methods for Removing Unwanted Variation Based on Negative Controls." Statistica Sinica, 31(3), 1145-1166. doi: 10.5705/ss.202018.0345

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