R.approx {netgwas} | R Documentation |
The expectation of covariance using approximation method
Description
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).
Usage
R.approx(y, Z = NULL, Sigma=NULL, rho = NULL, ncores = NULL )
Arguments
y |
An ( |
Z |
A ( |
Sigma |
The covariance matrix of the latent variable given the data. If |
rho |
A (non-negative) regularization parameter to calculate |
ncores |
If |
Value
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 |
Author(s)
Pariya Behrouzi and Ernst C. Wit
Maintainer: Pariya Behrouzi pariya.behrouzi@gmail.com
References
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.
Examples
## Not run:
D <- simgeno(p = 90, n = 50, k = 3)
R.approx(D$data)
## End(Not run)