f.K.fold: Subset the data into K fold, training and test data.
In JPEN: Covariance and Inverse Covariance Matrix Estimation Using Joint Penalty
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
K-fold subsetting.
Usage
1
f.K.fold(Nobs, K = 5)
Arguments
Nobs
n is number of observations
K
K is number of folds, typically 5 fold.
Details
K-fold subset of observations into training and test data.
Value
Returns the index for K-fold training and test data subsets.
Author(s)
Ashwini Maurya, Email: mauryaas@msu.edu
References
A Well Conditioned and Sparse Estimate of Covariance and Inverse Covariance Matrix Using Joint Penalty. Submitted.
http://arxiv.org/pdf/1412.7907v2.pdf
Examples
1
n=100;K=5;cv=f.K.fold(n,K);
Example output
Loading required package: mvtnorm
JPEN documentation built on May 2, 2019, 5:54 a.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
K-fold subsetting.
Usage
1 | f.K.fold(Nobs, K = 5)
|
Arguments
Nobs |
n is number of observations |
K |
K is number of folds, typically 5 fold. |
Details
K-fold subset of observations into training and test data.
Value
Returns the index for K-fold training and test data subsets.
Author(s)
Ashwini Maurya, Email: mauryaas@msu.edu
References
A Well Conditioned and Sparse Estimate of Covariance and Inverse Covariance Matrix Using Joint Penalty. Submitted. http://arxiv.org/pdf/1412.7907v2.pdf
Examples
1 | n=100;K=5;cv=f.K.fold(n,K);
|
Example output
Loading required package: mvtnorm
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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