R: Meinshausen & Buhlmann graph estimation

huge.mb {huge} | R Documentation

## Meinshausen & Buhlmann graph estimation

### Description

See more details in `huge`

### Usage

    huge.mb(x, lambda = NULL, nlambda = NULL, lambda.min.ratio = NULL,
      scr = NULL, scr.num = NULL, idx.mat = NULL, sym = "or",
      verbose = TRUE)

### Arguments

`x` | There are 2 options: (1) `x` is an `n` by `d` data matrix (2) a `d` by `d` sample covariance matrix. The program automatically identifies the input matrix by checking the symmetry. (`n` is the sample size and `d` is the dimension).
`lambda` | A sequence of decreasing positive numbers to control the regularization when `method = "mb"`, `"glasso"` or `"tiger"`, or the thresholding in `method = "ct"`. Typical usage is to leave the input `lambda = NULL` and have the program compute its own `lambda` sequence based on `nlambda` and `lambda.min.ratio`. Users can also specify a sequence to override this. When `method = "mb"`, `"glasso"` or `"tiger"`, use with care - it is better to supply a decreasing sequence values than a single (small) value.
`nlambda` | The number of regularization/thresholding parameters. The default value is `30` for `method = "ct"` and `10` for `method = "mb"`, `"glasso"` or `"tiger"`.
`lambda.min.ratio` | If `method = "mb"`, `"glasso"` or `"tiger"`, it is the smallest value for `lambda`, as a fraction of the upperbound (`MAX`) of the regularization/thresholding parameter which makes all estimates equal to `0`. The program can automatically generate `lambda` as a sequence of length = `nlambda` starting from `MAX` to `lambda.min.ratio*MAX` in log scale. If `method = "ct"`, it is the largest sparsity level for estimated graphs. The program can automatically generate `lambda` as a sequence of length = `nlambda`, which makes the sparsity level of the graph path increases from `0` to `lambda.min.ratio` evenly.The default value is `0.1` when `method = "mb"`, `"glasso"` or `"tiger"`, and 0.05 `method = "ct"`.
`scr` | If `scr = TRUE`, the lossy screening rule is applied to preselect the neighborhood before the graph estimation. The default value is `FALSE`. NOT applicable when `method = "ct"`, "mb", or "tiger".
`scr.num` | The neighborhood size after the lossy screening rule (the number of remaining neighbors per node). ONLY applicable when `scr = TRUE`. The default value is `n-1`. An alternative value is `n/log(n)`. ONLY applicable when `scr = TRUE` and `method = "mb"`.
`idx.mat` | Index matrix for screening.
`sym` | Symmetrize the output graphs. If `sym = "and"`, the edge between node `i` and node `j` is selected ONLY when both node `i` and node `j` are selected as neighbors for each other. If `sym = "or"`, the edge is selected when either node `i` or node `j` is selected as the neighbor for each other. The default value is `"or"`. ONLY applicable when `method = "mb"` or "tiger".
`verbose` | If `verbose = FALSE`, tracing information printing is disabled. The default value is `TRUE`.

### See Also

`huge`, and `huge-package`.

[Package huge version 1.3.2 Index]