Description Usage Arguments Details Value Author(s) References See Also Examples
This function implements the Gibbs sampling method within 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).
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y |
An (n \times p) matrix or a |
theta |
A p \times p precision matrix. Default is a diagonal matrix. |
gibbs.iter |
The number of burn-in for the Gibbs sampling. The default value is 1000. |
mc.iter |
The number of Monte Carlo samples to calculate the conditional expectation. The default value is 500. |
ncores |
If |
verbose |
If |
This function calculates \bar{R} using Gibbs sampling method within the E-step of EM algorithm, where
\bar{R} = n ^ {-1} ∑_{i=1}^{n} E( Z^{(i)} Z^{(i)t} | y^{(i)}, \hat{Θ}^{(m)})
which n is the number of sample size and Z is the latent variable which is obtained from Gaussian copula graphical model.
ES |
Expectation of covariance matrix ( diagonal scaled to 1) of the Gaussian copula graphical model |
Pariya Behrouzi, Danny Arends and Ernst C. Wit
Maintainers: 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.
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