Description Usage Arguments Value Author(s) References See Also Examples
This function implements the approximation method within the Gaussian copula graphical model to estimate the conditional expectation for the data that not follow Gaussianity assumption (e.g. ordinal, discrete, continuous non-Gaussian, or mixed dataset).
1 |
y |
An (n \times p) matrix or a |
Z |
A (n \times p) matrix which is a transformation of the data via the Gaussian copula. If |
Sigma |
The covariance matrix of the latent variable given the data. If |
rho |
A (non-negative) regularization parameter to calculate |
ncores |
If |
ES |
Expectation of covariance matrix( diagonal scaled to 1) of the Gaussian copula graphical model. |
Z |
New transformation of the data based on given or default |
Pariya Behrouzi and Ernst C. Wit
Maintainer: Pariya Behrouzi pariya.behrouzi@gmail.com
P. Behrouzi and E. C. Wit. Detecting Epistatic Selection with Partially Observed Genotype Data Using Copula Graphical Models. arXiv, 2016.
1 2 3 4 5 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.