ruvimpute: General imputation framework.
In dcgerard/vicar: Various Ideas for Confounder Adjustment in Regression

Description Usage Arguments Value Author(s) References

General imputation framework.

ruvimpute(
  Y,
  X,
  ctl,
  k = NULL,
  impute_func = em_miss,
  impute_args = list(),
  cov_of_interest = ncol(X),
  include_intercept = TRUE,
  do_variance = FALSE
)

`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_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`.
`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.
`do_variance`	A logical. Does `impute_func` also return estimates of the column-specific variances?

beta2hat The estimates of the coefficients of the covariates of interest that do not correspond to control genes.

betahat_long The estimates of the coefficients. Those corresponding to control genes are set to 0.

sebetahat If do_variance = TRUE, then these are the "standard errors" of beta2hat (but not really).

tstats If do_variance = TRUE, then these are the "t-statistics" of beta2hat (but not really).

pvalues If do_variance = TRUE, then these are the "p-values" of tstats (but not really).

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.