This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components

1 2 3 4 |

`fit` |
Object returned by superpc.train |

`data` |
Data object of form described in superpc.train documentation |

`n.threshold` |
Number of thresholds to consider. Default 20. |

`n.fold` |
Number of cross-validation folds. default is around 10 (program pick a convenient value based on the sample size |

`folds` |
List of indices of cross-validation folds (optional) |

`n.components` |
Number of cross-validation components to use: 1,2 or 3. |

`min.features` |
Minimum number of features to include, in determining range for threshold. Default 5. |

`max.features` |
Maximum number of features to include, in determining range for threshold. Default is total number of features in the dataset |

`compute.fullcv` |
Should full cross-validation be done? |

`compute.preval` |
Should full pre-validation be done? |

`xl.mode` |
Used by Excel interface only |

`xl.time` |
Used by Excel interface only |

`xl.prevfit` |
Used by Excel interface only |

This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components. To avoid prolems with fitting Cox models to samll validation datastes, it uses the "pre-validation" approach of Tibshirani and Efron (2002)

list(threshold = th, nonzero = nonzero, scor = out, scor.preval = out.preval, folds = folds, featurescores.folds = featurescores.folds, v.preval = cur2, type = type, call = this.call)

`threshold ` |
Vector of thresholds considered |

`nonzero` |
Number of features exceeding each value of the threshold |

`scor.preval` |
Likelihood ratio scores from pre-validation |

`scor` |
Full CV scores |

`folds` |
Indices of CV folds used |

`featurescores.folds` |
Feature scores for each fold |

`v.preval` |
The pre-validated predictors |

`type ` |
problem type |

`call ` |
calling sequence |

Eric Bair and Robert Tibshirani

1 2 3 4 5 6 7 8 9 10 11 | ```
set.seed(332)
x<-matrix(rnorm(1000*40),ncol=40)
y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
censoring.status<- sample(c(rep(1,30),rep(0,10)))
featurenames <- paste("feature",as.character(1:1000),sep="")
data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
a<- superpc.train(data, type="survival")
aa<-superpc.cv(a,data)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.