j0.multi: Function returning an overall point j0 of degeneration into...
In TopKLists: Inference, Aggregation and Visualization for Top-K Ranked Lists

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

Moderate deviation-based calculation of an overall point j_0 of degeneration into noise for multiple ranked lists. The function takes a matrix of ordered lists and estimates a j_0 for each pair of the input lists (columns), with repect to the preselected distance parameter δ. This function combines the functions compute.stream and prepare.Idata.

1	j0.multi(lists, d, v)

`lists`	Input data frame, where each column represents one list of ordered items
`d`	The maximal distance of an object's rank positions when two lists are compared. When the distance between the respective rank positions of the object is smaller or equal `d`, then the object is assigned the value 1, otherwise 0
`v`	Parameter for estimating j_0

The smaller d, the stronger the assumption about the concordance of any two lists (d=0 is assuming identical rankings of an object)

A list containing the maximal estimated indices of information degradation j_0 through all combinations of L lists:

`maxK`	Maximal estimated k through all combinations of two lists
`L`	Data frame of estimated j_0 for each pairwise comparison
`Idata`	Data stream vector of zeros and ones

Eva Budinska <budinska@iba.muni.cz>, Michael G. Schimek <michael.schimek@medunigraz.at>

Hall, P. and Schimek, M. G. (2012). Moderate deviation-based inference for random degeneration in paired rank lists. J. Amer. Statist. Assoc., 107, 661-672.

compute.stream, prepare.idata

set.seed(4657)

lists <- data.frame(L1=c("A","B","C","D","E","F","G","H","J","I","K","L","M","N"))
lists$L2 <- c("B","C","A","E","G","F","G","J","K","L","M","N","I","H")
lists$L3 <- sample(LETTERS[1:14])
res.j0.temp = j0.multi(lists, d=5, v=3)