AggregateBayes {GeneSelector} | R Documentation

## Bayesian aggregation of repeated rankings

### Description

The aggregated rank results from a posterior characteristic (argument `posteriorfun` below). The discrete prior is symmetrically centered around the rank obtained from the original dataset. The Likelihood is based on a normal distribution with variance `sigma` (s. below).

### Usage

    AggregateBayes(RR, S, tau, sigma = c("MAD", "sd"),
                    posteriorfun = c("mode", "mean", "median", "quantile"),
                    q = NULL)

### Arguments

`RR` | An object of class `RepeatRanking`.
`S` | Either an object of class `StabilityLm` or `StabilityOverlap`.
`tau` | The prior variance. Controls the confidence in the rank obtained from the original dataset. Should not be too large (<=1) in order to save computing time.
`sigma` | How the standard deviation for the Likelihood is to be estimated from the data (=ranks from perturbed datasets). `"MAD"` is a (weighted) MAD, `"sd"` a (weighted) standard deviation.
`posteriorfun` | Which statistic should be applied to the posterior distribution as a summary. If `"quantile"` is chosen, then it should be specified via the argument `q`.
`q` | The posterior quantile used as summary statistic. Only used if `posteriorfun` is `"quantile"`

### Details

The prior has support only in the range `[r0-2*tau;r0+2*tau]`, where `r0` is the prior mode (rank from the original dataset).
The weights for the estimation of `sigma` decrease linearly with decreasing similarity of perturbed dataset and original dataset as measured by Stability Measures (object `S`).

### Value

An object of class AggregatedRanking.

### Author(s)

Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### See Also

GetRepeatRanking, GetStabilityLm, GetStabilityOverlap, AggregateSimple

### Examples

    ## 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(xx, yy, k=1)
    ### Get all rankings
    loor_ordT <- GetRepeatRanking(ordT, loo)
    ### compute stability measure
    stab_overlap <- GetStabilityOverlap(loor_ordT, decay="linear")
    ### aggregate rankings
    agg_ordT <- AggregateBayes(loor_ordT, stab_overlap, tau=1)

[Package GeneSelector version 1.2.0 Index]