ConditionalDcorr¶
- class hyppo.conditional.ConditionalDcorr(compute_distance='euclidean', use_cov=True, bandwidth=None, **kwargs)¶
Conditional Distance Covariance/Correlation (CDcov/CDcorr) test statistic and p-value.
CDcorr is a measure of dependence between two paired random matrices given a third random matrix of not necessarily equal dimensions 1. The coefficient is 0 if and only if the matrices are independent given third matrix.
- Parameters
compute_distance (
str,callable, orNone, default:"euclidean") -- A function that computes the distance among the samples within each data matrix. Valid strings forcompute_distanceare, as defined insklearn.metrics.pairwise_distances,From scikit-learn: [
"euclidean","cityblock","cosine","l1","l2","manhattan"] See the documentation forscipy.spatial.distancefor details on these metrics.From scipy.spatial.distance: [
"braycurtis","canberra","chebyshev","correlation","dice","hamming","jaccard","kulsinski","mahalanobis","minkowski","rogerstanimoto","russellrao","seuclidean","sokalmichener","sokalsneath","sqeuclidean","yule"] See the documentation forscipy.spatial.distancefor details on these metrics.
Set to
Noneor"precomputed"ifxandyare already distance matrices. To call a custom function, either create the distance matrix before-hand or create a function of the formmetric(x, **kwargs)wherexis the data matrix for which pairwise distances are calculated and**kwargsare extra arguements to send to your custom function.use_cov (
bool,) -- If True, then the statistic will compute the covariance rather than the correlation.bandwith (
str,scalar,1d-array) -- The method used to calculate the bandwidth used for kernel density estimate of the conditional matrix. This can be ‘scott’, ‘silverman’, a scalar constant or a 1d-array with lengthrwhich is the dimensions of the conditional matrix. If None (default), ‘scott’ is used.**kwargs -- Arbitrary keyword arguments for
compute_distance.
References
- 1
Xueqin Wang, Wenliang Pan, Wenhao Hu, Yuan Tian, and Heping Zhang. Conditional distance correlation. Journal of the American Statistical Association, 110(512):1726–1734, 2015.
Methods Summary
|
Helper function that calculates the CDcov/CDcorr test statistic. |
|
Calculates the CDcov/CDcorr test statistic and p-value. |
- ConditionalDcorr.statistic(x, y, z)¶
Helper function that calculates the CDcov/CDcorr test statistic.
- Parameters
x,y,z (
ndarrayoffloat) -- Input data matrices.x,yandzmust have the same number of samples. That is, the shapes must be(n, p),(n, q)and(n, r)where n is the number of samples and p, q, and r are the number of dimensions. Alternatively,xandycan be distance matrices andzcan be a similarity matrix where the shapes must be(n, n).- Returns
stat (
float) -- The computed CDcov/CDcorr statistic.
- ConditionalDcorr.test(x, y, z, reps=1000, workers=1, random_state=None)¶
Calculates the CDcov/CDcorr test statistic and p-value.
- Parameters
x,y,z (
ndarrayoffloat) -- Input data matrices.x,yandzmust have the same number of samples. That is, the shapes must be(n, p),(n, q)and(n, r)where n is the number of samples and p, q, and r are the number of dimensions. Alternatively,xandycan be distance matrices andzcan be a similarity matrix where the shapes must be(n, n).reps (
int, default:1000) -- The number of replications used to estimate the null distribution when using the permutation test used to calculate the p-value.workers (
int, default:1) -- The number of cores to parallelize the p-value computation over. Supply-1to use all cores available to the Process.random_state (
int, default:None) -- The random_state for permutation testing to be fixed for reproducibility.
- Returns
