Description Usage Arguments Details Value Author(s) References
RUV-inverse as described in Gagnon-Bartsch et al (2013) is just
RUV4 using the maximum number of confounders allowed by the model,
followed by estimating the variances using a method-of-moments type
approach. vruvinv
is similar in spirit to this by using the
maximum number of confounders allowed by the model, but we still
estimate the variance inflation using the confounders. We force
limmashrink = TRUE
, otherwise there is only one degree of
freedom to estimate the variance.
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 |
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. |
likelihood |
Either |
include_intercept |
A logical. If |
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. |
adjust_bias |
A logical. Should we also use the control genes
to adjust for bias ( |
Hence, this is just a wrapper for vruv4
, but with a
special choice for the number of confounders and forcing
limmashrink = TRUE
.
See vruv4
for the elements returned.
David Gerard
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
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