coef_ols: Compute OLS coefficients
In scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data
coef_ols R Documentation
Compute OLS coefficients
Description
If the design matrix has full column-rank, then use the normal
least squares estimate. Otherwise, use the Moore-Penrose inverse
to compute the least squares estimate.
Usage
coef_ols(y, x)
Arguments
y
Target vector (n x 1)/matrix (n x m)
x
Design matrix (n x p)
Value
Vector of OLS coefficients
scregclust documentation built on April 4, 2025, 3:03 a.m.
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| coef_ols | R Documentation |
Compute OLS coefficients
Description
If the design matrix has full column-rank, then use the normal least squares estimate. Otherwise, use the Moore-Penrose inverse to compute the least squares estimate.
Usage
coef_ols(y, x)
Arguments
y |
Target vector (n x 1)/matrix (n x m) |
x |
Design matrix (n x p) |
Value
Vector of OLS coefficients
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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