asggm-internal {AdaptiveSparsity}

asggm internal functions

Description

These are the fitting and initialization functions used by asggm. These should generally not be used directly.

Usage

rCSL(x, iterations = 500, init = NULL, epsilon = 1e-05, ansL = NULL)
genL(kNodes, spP)
genData(L, nSamples)

Arguments

x

design matrix

iterations

number of iterations of the algorithm to run.

init

optional initialization, for instance, the cholesky of x. If NULL, it defaults to the cholesky of x.

epsilon

amount to add for numerical stability.

ansL

kNodes

spP

L

L created by genL

nSamples

number of samples.

Details

rCSL calls the C++ code to compute the Wong EM algorithm. genL and genData are used together to create example data.

Value

rCSL returns a list with the following components:

References

Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. “Adaptive Sparsity in Gaussian Graphical Models.”In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.

See Also

asggm, which should be used directly instead of these methods