mdm_ica {EDMeasure}  R Documentation 
mdm_ica
performs independent component analysis by minimizing mutual dependence measures
of all univariate components in X
.
mdm_ica(X, num_lhs = NULL, type = "comp", num_bo = NULL, kernel = "exp",
algo = "par")
X 
A matrix or data frame, where rows represent samples, and columns represent components. 
num_lhs 
The number of points generated by Latin hypercube sampling. If omitted, an adaptive number is used. 
type 
The type of mutual dependence measures, including

num_bo 
The number of points evaluated by Bayesian optimization. 
kernel 
The kernel of the underlying Gaussian process in Bayesian optimization, including

algo 
The algorithm of optimization, including

mdm_ica
returns a list including the following components:
theta 
The rotation angles of the estimated unmixing matrix. 
W 
The estimated unmixing matrix. 
obj 
The objective value of the estimated independence components. 
S 
The estimated independence components. 
Jin, Z., and Matteson, D. S. (2017). Generalizing Distance Covariance to Measure and Test Multivariate Mutual Dependence. arXiv preprint arXiv:1709.02532. https://arxiv.org/abs/1709.02532.
Pfister, N., et al. (2018). Kernelbased tests for joint independence. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1), 531. http://dx.doi.org/10.1111/rssb.12235.
# X is a 10 x 3 matrix with 10 samples and 3 components
X < matrix(rnorm(10 * 3), 10, 3)
# deflation algorithm
mdm_ica(X, type = "asym", algo = "def")
# parallel algorithm
mdm_ica(X, type = "asym", algo = "par")
## Not run:
# bayesian optimization with exponential kernel
mdm_ica(X, type = "sym", num_bo = 1, kernel = "exp", algo = "par")
# bayesian optimization with matern kernel
mdm_ica(X, type = "comp", num_bo = 1, kernel = "mat", algo = "par")
## End(Not run)