Description Usage Arguments Details Value References See Also Examples

View source: R/predict.cv.gcdclust.R

This function makes predictions from a cross-validated gcdclust model,
using the stored `"gcdclust.fit"`

object, and the optimal value
chosen for `lambda`

.

1 2 |

`object` |
fitted |

`newx` |
matrix of new values for |

`s` |
value(s) of the penalty parameter |

`...` |
not used. Other arguments to predict. |

This function makes it easier to use the results of cross-validation to make a prediction.

The object returned depends the ... argument which is passed on
to the `predict`

method for `gcdclust`

objects.

HHSVM-CEN
Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," *Journal of Computational and Graphical Statistics*, 22, 396-415.

BugReport: http://code.google.com/p/gcdclust/

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized
linear models via coordinate descent," *Journal of Statistical Software, 33, 1.*

http://www.jstatsoft.org/v33/i01/

`cv.gcdclust`

, and `coef.cv.gcdclust`

methods.

1 2 3 4 5 6 7 | ```
data(FHT)
set.seed(2011)
cv=cv.gcdclust(FHT$x, FHT$y,
lambda2 = 1, pred.loss="misclass",
lambda.factor=0.05,nfolds=5,KK=2)
pre = predict(cv$gcdclust.fit, newx = FHT$x,
s = cv$lambda.1se, type = "class",KK=2)
``` |

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