Description Usage Arguments Value Author(s) References See Also Examples

A matrix storing all rankings is centered rowwise (=genewise), and then approximated using only the first singular value and the first singular vectors (s. Golub and Van Loan (1983) for details about the SVD). The rowwise mean vector is added afterwards, and the rowwise mean are finally used as aggregation. A weighting scheme giving more weight to top genes is incorporated by an (iteratively) weighted SVD, which is re-computed until convergence. Note that the SVD is closely related to principal component analysis, a standard tool for dimension reduction in high-dimensional datasets.

1 | ```
AggregateSVD(RR, weightscheme = c("original", "iterative"), decay = c("linear", "quadratic", "exponential"), alpha=1)
``` |

`RR` |
An object of class |

`weightscheme` |
If |

`decay` |
Argument controlling the weight decay of the weights
of the summands contributing to the stability measure. If |

`alpha` |
s. |

An object of class AggregatedRanking.

Martin Slawski

Anne-Laure Boulesteix

Golub, G., Van Loan, C. (1983)

Matrix Computations *John Hopkins University Press*

RepeatRanking, AggregateSimple, AggregatePenalty, AggregateMC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### gene expression
xx <- toydata[-1,]
### run RankingTstat
ordT <- RankingTstat(xx, yy, type="unpaired")
### Generate Leave-one-out Foldmatrix
loo <- GenerateFoldMatrix(y = yy, k=1)
### Get all rankings
loor_ordT <- RepeatRanking(ordT, loo)
### aggregate rankings
agg_svd_ordT <- AggregateSVD(loor_ordT, weightscheme = "iterative", decay = "linear")
toplist(agg_svd_ordT)
``` |

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