R: The graphical lasso (glasso) using sparse matrix output

huge.glasso {huge} | R Documentation

## The graphical lasso (glasso) using sparse matrix output

### Description

See more details in `huge`

### Usage

    huge.glasso(
      x,
      lambda = NULL,
      lambda.min.ratio = NULL,
      nlambda = NULL,
      scr = NULL,
      cov.output = FALSE,
      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.
`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"`.
`nlambda` | The number of regularization/thresholding parameters. The default value is `30` for `method = "ct"` and `10` for `method = "mb"`, `"glasso"` or `"tiger"`.
`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".
`cov.output` | If `cov.output = TRUE`, the output will include a path of estimated covariance matrices. ONLY applicable when `method = "glasso"`. Since the estimated covariance matrices are generally not sparse, please use it with care, or it may take much memory under high-dimensional setting. The default value is `FALSE`.
`verbose` | If `verbose = FALSE`, tracing information printing is disabled. The default value is `TRUE`.

### See Also

`huge`, and `huge-package`.

[Package huge version 1.3.5 Index]