brainconn.reference
.null_model_dir_sign¶
-
null_model_dir_sign
(W, bin_swaps=5, wei_freq=0.1)[source]¶ This function randomizes an directed network with positive and negative weights, while preserving the degree and strength distributions. This function calls randmio_dir.m
Parameters: - W (NxN
numpy.ndarray
) – directed weighted connection matrix - bin_swaps (int) – average number of swaps in each edge binary randomization. Default value is 5. 0 swaps implies no binary randomization.
- wei_freq (float) –
frequency of weight sorting in weighted randomization. 0<=wei_freq<1. wei_freq == 1 implies that weights are sorted at each step. wei_freq == 0.1 implies that weights sorted each 10th step (faster,
default value)wei_freq == 0 implies no sorting of weights (not recommended)
Returns: - W0 (NxN
numpy.ndarray
) – randomized weighted connection matrix - R (4-tuple of floats) – Correlation coefficients between strength sequences of input and output connection matrices, rpos_in, rpos_out, rneg_in, rneg_out
Notes
- The value of bin_swaps is ignored when binary topology is fully
- connected (e.g. when the network has no negative weights).
- Randomization may be better (and execution time will be slower) for
- higher values of bin_swaps and wei_freq. Higher values of bin_swaps may enable a more random binary organization, and higher values of wei_freq may enable a more accurate conservation of strength sequences.
- R are the correlation coefficients between positive and negative
- in-strength and out-strength sequences of input and output connection matrices and are used to evaluate the accuracy with which strengths were preserved. Note that correlation coefficients may be a rough measure of strength-sequence accuracy and one could implement more formal tests (such as the Kolmogorov-Smirnov test) if desired.
- W (NxN