weights_gausscopula: Computation of the weight matrix in a gaussian copula...

In saturnin: Spanning Trees Used for Network Inference

Description

The function computes the weights of all edges in a gaussian copula setting. The result should be used in edge.prob with argument log set to FALSE. The function brings the values of all variables back to [0;1] by computing univariate empirical cdf functions. The prior distribution for the correlation of the bivariate gaussian copulas prior can be set to either "uniform" or "beta". Beta prior is understood as a beta distribution with a change of variables to bring it back to [-1;1]. Computation can be parallelized by setting nbcores to more than 2. Parallelization relies on parallel.

Usage

weights_gausscopula(data, prior_type = "uniform", a = 1, b = 1, nbcores = 1)

Arguments

data	Matrix containing the data.
prior_type	Prior to be used for the correlation.
a	Shape parameter 1 for beta prior.
b	Shape parameter 2 for beta prior.
nbcores	Number of cores to be used in parallelized computation.

Value

W	weight matrix.

Author(s)

Lo<c3><af>c Schwaller

References

This package implements the method described in the paper "Bayesian Inference of Graphical Model Structures Using Trees" by L. Schwaller, S. Robin, M. Stumpf, 2015 (submitted and availavable on arXiv).

Examples

library('saturnin')
data(data_multinomial)

W <- weights_gausscopula(data_multinomial)

prob <- edge.prob(W, log = FALSE)

Example output

saturnin documentation built on May 1, 2019, 10:18 p.m.