Compute values of likelihood ratio test from supervised principal components fit

1 | ```
superpc.lrtest.curv(object, data, newdata, n.components = 1, threshold = NULL, n.threshold = 20)
``` |

`object` |
Object returned by superpc.train |

`data` |
List of training data, of form described in superpc.train documentation |

`newdata` |
List of test data; same form as training data |

`n.components` |
Number of principal components to compute. Should be 1,2 or 3. |

`threshold` |
Set of thresholds for scoresL default is n.threshold values equally spaced over the range of the feature scores |

`n.threshold` |
Number of thresholds to use; default 20. Should be 1,2 or 3. |

If it is a LIST, use

`lrtest ` |
Values of likelihood ratio test statistic |

`comp2 ` |
Description of 'comp2' |

`threshold` |
Thresholds used |

`num.features` |
Number of features exceeding threshold |

`type` |
Type of outcome variable |

`call` |
calling sequence |

Eric Bair and Robert Tibshirani

~put references to the literature/web site here ~

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
set.seed(332)
#generate some data
x<-matrix(rnorm(1000*20),ncol=20)
y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
censoring.status<- sample(c(rep(1,17),rep(0,3)))
censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
featurenames <- paste("feature",as.character(1:1000),sep="")
data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
a<- superpc.train(data, type="survival")
fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
aa<- superpc.lrtest.curv(a, data, data.test)
superpc.plot.lrtest(aa)
``` |

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