Description Usage Arguments Details Value Author(s) References Examples

SIS has been performed to select relevant gene expression variables. SIS ranks the importance of features according to their magnitude of marginal regression coefficients.

1 | ```
SIS.selection(X,Y, pred, scale = F)
``` |

`X` |
a data matrix ( |

`Y` |
a vector of length n giving the classes of the n observations. The classes must be coded as 1 or 0. |

`pred` |
number of relevant variable to select, |

`scale` |
If scale=TRUE, |

Sure Independence Screening (SIS) has been performed to select relevant gene expression
variables `pred`

such as `pred`

< `p`

. SIS refers to ranking features according to marginal
utility, namely, each feature is used independently as a predictor to decide its usefulness
for predicting the response. Precisely SIS ranks the importance of features according to
their magnitude of marginal regression coefficients.

Return a matrix (`nxpred`

) with only the `pred`

most relevant gene and all the observations

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

Fan, J. and Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society, 70, 849-911.

1 2 3 4 5 | ```
data("BreastCancer")
X<-scale(BreastCancer$X)
Y<-BreastCancer$Y
Xsis<-SIS.selection(X,Y,50)
``` |

lsplsGlm documentation built on July 27, 2017, 5:01 p.m.

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