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]