beta_regress: beta_regress
In xinchoubiology/Rcppsva: Rcpp Integration Surrogate Variable Analysis
Description
Usage
Arguments
Author(s)
Description
Linear regression on covariate of interest when comparing with other covariates
Usage
1
beta_regress(M, pv, svs, full = 0L)
Arguments
M
m x n expression matrix; Each row represents probes and each col means samples
pv
n x B design matrix; Each col means phenotype of interest, and if B >=2,
means we have B-1 permutations on covariates of interest
svs
n x (p-1) design matrix; Each row represent samples and each col means parameters
full
full output or coefficient only
Author(s)
Xin Zhou
xinchoubiology/Rcppsva documentation built on May 4, 2019, 1:06 p.m.
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Description Usage Arguments Author(s)
Description
Linear regression on covariate of interest when comparing with other covariates
Usage
1 | beta_regress(M, pv, svs, full = 0L)
|
Arguments
M |
m x n expression matrix; Each row represents probes and each col means samples |
pv |
n x B design matrix; Each col means phenotype of interest, and if B >=2, means we have B-1 permutations on covariates of interest |
svs |
n x (p-1) design matrix; Each row represent samples and each col means parameters |
full |
full output or coefficient only |
Author(s)
Xin Zhou
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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