# huge.mb: Meinshausen & Buhlmann graph estimation In huge: High-Dimensional Undirected Graph Estimation

## Description

See more details in `huge`

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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`.

`huge`, and `huge-package`.