nutriNetwork (version 0.2.0)

selectnet: Model selection for optimal graph estimation

Description

Estimate the optimal graph based on different information criteria .

Usage

selectnet(nutriNetwork.obj, opt.index= NULL, criteria= NULL, ebic.gamma=0.5,
		   ncores= NULL, verbose= TRUE)

Value

An obj with S3 class "selectnet" is returned:

opt.adj

The optimal graph selected from the graph path

opt.theta

The optimal precision matrix from the graph path

opt.sigma

The optimal covariance matrix from the graph path

ebic.scores

Extended BIC scores for regularization parameter selection at the EM convergence. Applicable if opt.index = NULL.

opt.index

The index of optimal regularization parameter.

opt.rho

The selected regularization parameter.

par.cor

A partial correlation matrix.

and anything else that is included in the input nutriNetwork.obj.

Arguments

nutriNetwork.obj

An object with S3 class "nutriNetwork"

opt.index

The program internally determines an optimal graph using opt.index= NULL. Otherwise, to manually choose an optimal graph from the graph path.

criteria

Model selection criteria. "ebic" and "aic" are available. BIC model selection can be calculated by fixing ebic.gamma = 0. Applicable only if opt.index= NULL.

ebic.gamma

The tuning parameter for ebic. Theebic.gamma = 0 results in bic model selection. The default value is 0.5. Applicable only opt.index= NULL.

ncores

The number of cores to use for the calculations. Using ncores = "all" automatically detects number of available cores and runs the computations in parallel.

verbose

If verbose = FALSE, printing information is disabled. The default value is TRUE. Applicable only opt.index= NULL.

Author

Pariya Behrouzi
Maintainer: Pariya Behrouzi

References

1. 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.
2. Behrouzi, P., and Wit, E. C. (2017c). netgwas: An R Package for Network-Based Genome-Wide Association Studies. arXiv preprint, arXiv:1710.01236.
3. Ibrahim, Joseph G., Hongtu Zhu, and Niansheng Tang. (2012). Model selection criteria for missing-data problems using the EM algorithm. Journal of the American Statistical Association. 4. D. Witten and J. Friedman. (2011). New insights and faster computations for the graphical lasso. Journal of Computational and Graphical Statistics, to appear.
5. J. Friedman, T. Hastie and R. Tibshirani. (2007). Sparse inverse covariance estimation with the lasso, Biostatistics.
6. Foygel, R. and M. Drton. (2010). Extended bayesian information criteria for Gaussian graphical models. In Advances in Neural Information Processing Systems, pp. 604-612.

Examples

######## toy example
data(vfit)
test_dat <- vfit[1:10, c("sex", "ani.pro", "veg.pro", "B6",
            "B12", "B9", "SPPB.total", "HandGrip"  )]
out_test <- nutriNetwork(test_dat, method = "gibbs")
sel_test <- selectnet(out_test)
########
# \dontshow{
out_check <- nutriNetwork(vfit, method ="npn")
sel_check <- selectnet(out_check)
# }
# \donttest{
 out <- nutriNetwork(vfit, method = "gibbs")
 sel <- selectnet(out)

 cl <- c(rep("gray70", 7), rep("green3",17), rep("red3",5))
 plot(sel, vis= "parcor.network", sign.edg = TRUE,
      vertex.color = cl, curve = TRUE, layout.tree= TRUE,
      root.node= c(26, 29), pos.legend= "bottomleft",
      cex.legend=1)
#diffeent visualization
plot(sel, vis= "parcor.network", sign.edg = TRUE, layout = NULL,
     vertex.color = cl, curve = TRUE, pos.legend= "topleft",
     cex.legend=1 )
  # }