GetStabilityDistance {GeneSelector} | R Documentation |
The similarity of two rankings is assessed by computing a weighted distance measure. This function implements weighted absolute- and squared distance, a weighted versions of Spearman's rank correlation coefficient and Kendall's tau. Note that Spearman's rank correlation coefficient is not a distance measure in the classical sense, but can be obtained as transformation of the squared distance.
GetStabilityDistance(RR, scheme = c("original", "pairwise"), measure = c("l1", "l2", "spearman", "kendall"), decay = c("linear", "quadratic", "exponential"), alpha = 1, ...)
RR |
An object of class |
scheme |
If |
measure |
The measure to be used. |
decay |
Argument controlling the weight decay of the weights
of the summands contributing to the stability measure. If |
alpha |
s. |
... |
Currently unused argument. |
An object of class StabilityDistance
Martin Slawski
Anne-Laure Boulesteix
Jurman, G., Merler, S., Barla, A., Paoli, S., Galea, A., Furlanello, C. (2008).
Algebraic stability indicators for ranked lists in molecular profiling. Bioinformatics 24, 258-264
DeConde, R. P., Hawley, S., Falcon, S., Clegg, N., Knudsen, B., Etzioni, R. (2006).
Combining results of microarray experiments: a rank aggregation approach. Statistical Applications in Genetics and
Molecular Biology 5, 15
## Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### get ranking ordT <- RankingTstat(xx, yy, type="unpaired") ### Generate Leave-One-Out loo <- GenerateFoldMatrix(y = yy, k=1) ### Repeat Ranking with t-statistic loor_ordT <- RepeatRanking(ordT, loo) ### assess stability stab_dis_ordT <- GetStabilityDistance(loor_ordT, scheme = "original", measure = "spearman", decay="linear") ### for a short summary summary(stab_dis_ordT, display = "all")