R: Graph inference

huge.inference {huge} | R Documentation

## 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.

### See Also

`huge`, and `huge-package`.

### 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")

[Package huge version 1.3.5 Index]