R.gibbs | R Documentation |
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).
R.gibbs(y, theta, gibbs.iter = 1000, mc.iter = 500,
ncores = NULL, verbose = TRUE)
y |
An ( |
theta |
A |
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} \sum_{i=1}^{n} E( Z^{(i)} Z^{(i)t} | y^{(i)}, \hat{\Theta}^{(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
1. Behrouzi, P., Arends, D., and Wit, E. C. (2023). netgwas: An R Package for Network-Based Genome-Wide Association Studies. The R journal, 14(4), 18-37.
2. Behrouzi, P., and Wit, E. C. (2019). Detecting epistatic selection with partially observed genotype data by using copula graphical models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(1), 141-160.
D <- simgeno(p = 100, n = 50, k = 3)
R.gibbs(D$data, ncores=1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.