ensemble.splsdaCV: Estimate cross-validation error using cross-validation
In singha53/amritr: Accessory Functions Used by Amrit Singh
Description
Estimate cross-validation error using cross-validation
Usage
1
2
ensemble.splsdaCV(X, Y, keepXList, ncomp, M, folds, progressBar, filter,
topranked)
Arguments
X
- list of training datasets (nxpi); i number of elements
Y
- n-vector of class labels
keepXList
= list of keepX
M
- # of folds
folds
- list of length M, where each element contains the indices for samples for a given fold
progressBar
(TRUE/FALSE) - show progress bar or not
filter
- "none" or "p.value"
topranked
- # of significant features to use to build classifier
singha53/amritr documentation built on July 21, 2019, 3:46 p.m.
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Description
Estimate cross-validation error using cross-validation
Usage
1 2 | ensemble.splsdaCV(X, Y, keepXList, ncomp, M, folds, progressBar, filter,
topranked)
|
Arguments
X |
- list of training datasets (nxpi); i number of elements |
Y |
- n-vector of class labels |
keepXList |
= list of keepX |
M |
- # of folds |
folds |
- list of length M, where each element contains the indices for samples for a given fold |
progressBar |
(TRUE/FALSE) - show progress bar or not |
filter |
- "none" or "p.value" |
topranked |
- # of significant features to use to build classifier |
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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