mvpa2.measures.rsa.rankdata

mvpa2.measures.rsa.rankdata(a, method='average')

Assign ranks to data, dealing with ties appropriately.

Ranks begin at 1. The method argument controls how ranks are assigned to equal values. See [R16] for further discussion of ranking methods.

Parameters:

a : array_like

The array of values to be ranked. The array is first flattened.

method : str, optional

The method used to assign ranks to tied elements. The options are ‘average’, ‘min’, ‘max’, ‘dense’ and ‘ordinal’.

‘average’:

The average of the ranks that would have been assigned to all the tied values is assigned to each value.

‘min’:

The minimum of the ranks that would have been assigned to all the tied values is assigned to each value. (This is also referred to as “competition” ranking.)

‘max’:

The maximum of the ranks that would have been assigned to all the tied values is assigned to each value.

‘dense’:

Like ‘min’, but the rank of the next highest element is assigned the rank immediately after those assigned to the tied elements.

‘ordinal’:

All values are given a distinct rank, corresponding to the order that the values occur in a.

The default is ‘average’.

Returns:

ranks : ndarray

An array of length equal to the size of a, containing rank scores.

References

[R16]

(1, 2) “Ranking”, http://en.wikipedia.org/wiki/Ranking

Examples

>>> from scipy.stats import rankdata
>>> rankdata([0, 2, 3, 2])
array([ 1. ,  2.5,  4. ,  2.5])
>>> rankdata([0, 2, 3, 2], method='min')
array([ 1,  2,  4,  2])
>>> rankdata([0, 2, 3, 2], method='max')
array([ 1,  3,  4,  3])
>>> rankdata([0, 2, 3, 2], method='dense')
array([ 1,  2,  3,  2])
>>> rankdata([0, 2, 3, 2], method='ordinal')
array([ 1,  2,  4,  3])