R.approx | R Documentation |
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).
R.approx(y, Z = NULL, Sigma=NULL, rho = NULL, ncores = NULL )
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 |
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
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.
## Not run:
D <- simgeno(p = 90, n = 50, k = 3)
R.approx(D$data)
## End(Not run)
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