giddy.rank.Tau_Local¶
- class giddy.rank.Tau_Local(x, y)[source]¶
Local version of the classic Tau.
Decomposition of the classic Tau into local components.
- Parameters:
- xarray
(n, ), first variable.
- yarray
(n, ), second variable.
Notes
The equation for calculating local concordance statistic can be found in [Rey16] Equation (9).
Examples
>>> import libpysal as ps >>> import numpy as np >>> from giddy.rank import Tau_Local,Tau >>> np.random.seed(10) >>> f = ps.io.open(ps.examples.get_path("mexico.csv")) >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)] >>> y = np.transpose(np.array([f.by_col[v] for v in vnames])) >>> r = y / y.mean(axis=0) >>> tau_local = Tau_Local(r[:,0],r[:,1]) >>> tau_local.tau_local array([-0.03225806, 0.93548387, 0.80645161, 0.74193548, 0.93548387, 0.74193548, 0.67741935, 0.41935484, 1. , 0.5483871 , 0.74193548, 0.93548387, 0.67741935, 0.74193548, 0.80645161, 0.74193548, 0.5483871 , 0.67741935, 0.74193548, 0.74193548, 0.5483871 , -0.16129032, 0.93548387, 0.61290323, 0.67741935, 0.48387097, 0.93548387, 0.61290323, 0.74193548, 0.41935484, 0.61290323, 0.61290323]) >>> tau_local.tau 0.6612903225806451 >>> tau_classic = Tau(r[:,0],r[:,1]) >>> tau_classic.tau 0.6612903225806451
- Attributes:
- nint
number of observations.
- taufloat
The classic Tau statistic.
- tau_localarray
(n, ), local concordance (local version of the classic tau).
- Sarray
(n ,n), concordance matrix, s_{i,j}=1 if observation i and j are concordant, s_{i,j}=-1 if observation i and j are discordant, and s_{i,j}=0 otherwise.
Methods
__init__
(x, y)