OCV: Ordinary Cross-Validation for penalty parameter
In pmesperanca/mlevcm: ML-based Epidemiological Vector Control Monitoring using FDA techniques for NIRS data
Description
Usage
Arguments
Value
View source: R/fdaAuxFunctions.R
Description
Ordinary Cross-Validation method for optimising the penalty parameter.
Usage
1
OCV(XX, yy, newX, newY, optionsEst)
Arguments
XX
Training predictor matrix.
yy
Training response vector.
newX
Validation predictor matrix.
newY
Validation response matrix.
optionsEst
List of options for estimation.
Value
The value of lambda which minimises the Ordinary Cross-Validation criterion,
that is, minimises the RMSD (for Regression) or maximises the AUC (for Classification).
pmesperanca/mlevcm documentation built on March 17, 2021, 10:03 p.m.
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Description Usage Arguments Value
View source: R/fdaAuxFunctions.R
Description
Ordinary Cross-Validation method for optimising the penalty parameter.
Usage
1 | OCV(XX, yy, newX, newY, optionsEst)
|
Arguments
XX |
Training predictor matrix. |
yy |
Training response vector. |
newX |
Validation predictor matrix. |
newY |
Validation response matrix. |
optionsEst |
List of options for estimation. |
Value
The value of lambda which minimises the Ordinary Cross-Validation criterion, that is, minimises the RMSD (for Regression) or maximises the AUC (for Classification).
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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