# Return a list of the important predictors

### Description

Return a list of the important predictor

### Usage

1 2 | ```
superpc.listfeatures(data, train.obj, fit.red, fitred.cv = NULL,
num.features=NULL, component.number = 1)
``` |

### Arguments

`data` |
Data object |

`train.obj` |
Object returned by superpc.train |

`fit.red` |
Object returned by superpc.predict.red, applied to training set |

`fitred.cv` |
(Optional) object returned by superpc.predict.red.cv |

`num.features` |
Number of features to list. Default is all features. |

`component.number` |
Number of principal component (1,2, or 3) used to determine feature importance scores |

### Value

Returns matrix of features and their importance scores, in order of decreasing absolute value of importance score. The importance score is the correlation of the reduced predictor and the full supervised PC predictor. It also lists the raw score- for survival data, this is the Cox score for that feature; for regression, it is the standardized regression coefficient. If fitred.cv is supplied, the function also reports the average rank of the gene in the cross-validation folds, and the proportion of times that the gene is chosen (at the given threshold) in the cross-validation folds.

### Author(s)

Eric Bair and Rob Tibshirani

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
#generate some data
x<-matrix(rnorm(1000*40),ncol=40)
y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
censoring.status<- sample(c(rep(1,30),rep(0,10)))
censoring.status.test<- 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)
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")
fit.red<- superpc.predict.red(a,data, data.test, .6)
superpc.listfeatures(data, a, fit.red, num.features=20)
``` |