cv.multiRegression: cv.multiRegression
In fssemR: Fused Sparse Structural Equation Models to Jointly Infer Gene Regulatory Network
cv.multiRegression R Documentation
cv.multiRegression
Description
cv.multiRegression
Usage
cv.multiRegression(Xs, Ys, Sk, ngamma = 20, nfold = 5, n, p, k)
Arguments
Xs
eQTL matrices
Ys
Gene expression matrices
Sk
eQTL index of genes
ngamma
number of hyper-parameter in CV
nfold
CVfold number. Default 5/10
n
number of observations
p
number of genes
k
number of eQTLs
Value
gamma_min optimal gamma to minimize cross-validation error
fssemR documentation built on March 18, 2022, 7:24 p.m.
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| cv.multiRegression | R Documentation |
cv.multiRegression
Description
cv.multiRegression
Usage
cv.multiRegression(Xs, Ys, Sk, ngamma = 20, nfold = 5, n, p, k)
Arguments
Xs |
eQTL matrices |
Ys |
Gene expression matrices |
Sk |
eQTL index of genes |
ngamma |
number of hyper-parameter in CV |
nfold |
CVfold number. Default 5/10 |
n |
number of observations |
p |
number of genes |
k |
number of eQTLs |
Value
gamma_min optimal gamma to minimize cross-validation error
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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