frites.estimator.DcorrEstimator#

class frites.estimator.DcorrEstimator(implementation='auto', verbose=None)[source]#

Distance correlation-based estimator.

This estimator can be used to estimate the correlation between two continuous variables (mi_type=’cc’).

Parameters
implementation{‘auto’, ‘frites’, ‘dcor’}

Choose wich implementation of the distance correlation to use. If ‘frites’ a home-made version is going to be used. If ‘dcor’, the one of the dcorr package is going to be preferred (see for installation https://dcor.readthedocs.io/).

Methods

estimate(x, y[, z, categories])

Estimate the distance correlation between two variables.

get_function()

Get the function to execute according to the input parameters.

estimate(x, y, z=None, categories=None)[source]#

Estimate the distance correlation between two variables.

This method is made for computing the correlation on 3D variables (i.e (n_var, n_mv, n_samples)) where n_var is an additional dimension (e.g times, times x freqs etc.)n_mv is a multivariate axis and n_samples the number of samples.

Parameters
x, ynumpy:array_like

Array of shape (n_var, n_mv, n_samples).

categoriesnumpy:array_like | python:None

Row vector of categories. This vector should have a shape of (n_samples,) and should contains integers describing the category of each sample.

Returns
corrnumpy:array_like

Array of correlation of shape (n_categories, n_var).

Examples using estimate:

Trial-resampling: correcting for unbalanced designs

Trial-resampling: correcting for unbalanced designs

Estimator comparison

Estimator comparison
get_function()[source]#

Get the function to execute according to the input parameters.

This can be particularly useful when computing correlation in parallel as it avoids to pickle the whole estimator and therefore, leading to faster computations.

The returned function has the following signature :

  • fcn(x, y, *args, categories=None, **kwargs)

and return an array of shape (n_categories, n_var).

Examples using frites.estimator.DcorrEstimator#

Estimate the Dynamic Functional Connectivity

Estimate the Dynamic Functional Connectivity

Trial-resampling: correcting for unbalanced designs

Trial-resampling: correcting for unbalanced designs

Estimator comparison

Estimator comparison