Description Usage Arguments Value Author(s) References
General imputation framework.
1 2 3 4 5 6 7 8 9 10 11 |
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 |
k |
The rank of the underlying matrix. Used by
|
impute_func |
A function that takes as input a matrix names
|
impute_args |
A list of additional parameters to pass to
|
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 |
do_variance |
A logical. Does |
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.