GCV: Generalised 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
Generalised Cross-Validation method for optimising the penalty parameter.
Usage
1
GCV(XX, yy, penmat)
Arguments
XX
Training predictor matrix.
yy
Training response vector.
penmat
Penalty matrix.
Value
The value of lambda which minimises the Generalised Cross-Validation criterion.
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
Generalised Cross-Validation method for optimising the penalty parameter.
Usage
1 | GCV(XX, yy, penmat)
|
Arguments
XX |
Training predictor matrix. |
yy |
Training response vector. |
penmat |
Penalty matrix. |
Value
The value of lambda which minimises the Generalised Cross-Validation criterion.
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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