huge: huge.inference – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

huge.inference

Graph inference

Description

Implements the inference for high dimensional graphical models, including Gaussian and Nonparanormal graphical models We consider the problems of testing the presence of a single edge and the hypothesis is that the edge is absent.

Usage

huge.inference(data, T, adj, alpha = 0.05, type = "Gaussian",
  method = "score")

Arguments

`data`	The input `n` by `d` data matrix(`n` is the sample size and `d` is the dimension).
`T`	The estimated inverse of correlation matrix of the data.
`adj`	The adjacency matrix corresponding to the graph.
`alpha`	The significance level of hypothesis.The default value is `0.05`.
`type`	The type of input data. There are 2 options: `"Gaussian"` and `"Nonparanormal"`. The default value is `"Gaussian"`.
`method`	When using nonparanormal graphical model. Test method with 2 options: `"score"` and `"wald"`. The default value is `"score"`.

Details

For Nonparanormal graphical model we provide Score test method and Wald Test. However it is really slow for inferencing on Nonparanormal model, especially for large data.

Value

An object is returned:

`data`	The `n` by `d` data matrix from the input.
`p`	The `d` by `d` p-value matrix of hypothesis.
`error`	The type I error of hypothesis at alpha significance level.

References

1.Q Gu, Y Cao, Y Ning, H Liu. Local and global inference for high dimensional nonparanormal graphical models.
2.J Jankova, S Van De Geer. Confidence intervals for high-dimensional inverse covariance estimation. Electronic Journal of Statistics, 2015.

Examples

#generate data
L = huge.generator(n = 50, d = 12, graph = "hub", g = 4)

#graph path estimation using glasso
est = huge(L$data, method = "glasso")

#inference of Gaussian graphical model at 0.05 significance level
T = tail(est$icov, 1)[[1]]
out1 = huge.inference(L$data, T, L$theta)

#inference of Nonparanormal graphical model using score test at 0.05 significance level
T = tail(est$icov, 1)[[1]]
out2 = huge.inference(L$data, T, L$theta, type = "Nonparanormal")

#inference of Nonparanormal graphical model using wald test at 0.05 significance level
T = tail(est$icov, 1)[[1]]
out3 = huge.inference(L$data, T, L$theta, type = "Nonparanormal", method = "wald")

#inference of Nonparanormal graphical model using wald test at 0.1 significance level
T = tail(est$icov, 1)[[1]]
out4 = huge.inference(L$data, T, L$theta, 0.1, type = "Nonparanormal", method = "wald")

huge

High-Dimensional Undirected Graph Estimation

v1.3.4.1

GPL-2

Authors

Haoming Jiang, Xinyu Fei, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman, Xingguo Li, and Tuo Zhao

Initial release