Description Usage Arguments Value Author(s) References See Also
This model is fit using a two-step approach proposed in Gagnon-Bartsch et al (2013) modified to include estimating a variance inflation parameter. Rather than use OLS in the second step of this two-step procedure, we estimate the coefficients using Adaptive SHrinkage (ASH) (Stephens, 2016). In the current implementation, only the coefficients of one covariate can be estimated using ASH. The rest are regressed out using OLS.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
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 |
A non-negative integer.The number of unobserved confounders. If not specified and the R package sva is installed, then this function will estimate the number of hidden confounders using the methods of Buja and Eyuboglu (1992). |
cov_of_interest |
A positive integer. The column number of the
covariate in X whose coefficients you are interested in. The
rest are considered nuisance parameters and are regressed out
by OLS. |
likelihood |
Either |
ash_args |
A list of arguments to pass to ash. See
|
limmashrink |
A logical. Should we apply hierarchical
shrinkage to the variances ( |
degrees_freedom |
if |
include_intercept |
A logical. If |
gls |
A logical. Should we use generalized least squares
( |
fa_func |
A factor analysis function. The function must have
as inputs a numeric matrix |
fa_args |
A list. Additional arguments you want to pass to fa_func. |
use_factor |
A logical. Should we use the estimates of
|
Except for the list ruv2
, the values returned are
the exact same as in ash.workhorse
. See that
function for more details. Elements in the ruv2
are the
exact same as returned in vruv2
.
David Gerard
Buja, A. and Eyuboglu, N., 1992. "Remarks on parallel analysis." Multivariate behavioral research, 27(4), pp.509-540. doi: 10.1207/s15327906mbr2704_2
Gagnon-Bartsch, J., Laurent Jacob, and Terence P. Speed, 2013. "Removing unwanted variation from high dimensional data with negative controls." Berkeley: Department of Statistics. University of California. https://statistics.berkeley.edu/tech-reports/820
Stephens, Matthew. 2016. "False discovery rates: a new deal." Biostatistics 18 (2): 275–94. doi: 10.1093/biostatistics/kxw041
vruv2
for the variance inflation in RUV2 and
ash.workhorse
for the adaptive shrinkage
method.
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