Description Usage Arguments Details Value References See Also Examples

View source: R/mcPulsarSelect.R

Run pulsar using StARS' edge stability (or other criteria) to select an undirected graphical model over a lambda path.

1 2 3 4 |

`data` |
A |

`fun` |
pass in a function that returns a list representing |

`fargs` |
arguments to argument |

`criterion` |
A character vector of selection statistics. Multiple criteria can be supplied. Only StARS can be used to automatically select an optimal index for the lambda path. See details for additional statistics. |

`thresh` |
threshold (referred to as scalar |

`subsample.ratio` |
determine the size of the subsamples (referred to as |

`rep.num` |
number of random subsamples |

`seed` |
A numeric seed to force predictable subsampling. Default is NULL. Use for testing purposes only. |

`lb.stars` |
Should the lower bound be computed after the first |

`ub.stars` |
Should the upper bound be computed after the first |

`ncores` |
number of cores to use for subsampling. See |

`refit` |
Boolean flag to refit on the full dataset after pulsar is run. (see also |

The options for `criterion`

statistics are:

stars (Stability approach to regularization selection)

gcd (Graphet correlation distance, requires the orca package) see

`gcvec`

diss (Node-node dissimilarity) see

`graph.diss`

estrada (estrada class) see

`estrada.class`

nc (natural connectivity) see

`natural.connectivity`

sufficiency (Tandon & Ravikumar's sufficiency statistic)

an S3 object of class `pulsar`

with a named member for each stability metric run. Within each of these are:

summary: the summary statistic over

`rep.num`

graphs at each value of lambdacriterion: the stability criterion used

merge: the raw statistic over the

`rep.num`

graphs, prior to summarizationopt.ind: index (along the path) of optimal lambda selected by the criterion at the desired threshold. Will return

*0*if no optimum is found or`NULL`

if selection for the criterion is not implemented.

If `stars`

is included as a criterion then additional arguments include

lb.index: the lambda index of the lower bound at

*N=2*samples if`lb.stars`

flag is set to TRUEub.index: the lambda index of the upper bound at

*N=2*samples if`ub.stars`

flag is set to TRUE

call: the original function call

Müller, C. L., Bonneau, R., & Kurtz, Z. (2016). Generalized Stability Approach for Regularized Graphical Models. arXiv. http://arxiv.org/abs/1605.07072

Liu, H., Roeder, K., & Wasserman, L. (2010). Stability approach to regularization selection (stars) for high dimensional graphical models. Proceedings of the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS).

Zhao, T., Liu, H., Roeder, K., Lafferty, J., & Wasserman, L. (2012). The huge Package for High-dimensional Undirected Graph Estimation in R. The Journal of Machine Learning Research, 13, 1059–1062.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## Not run:
## Generate the data with huge:
library(huge)
p <- 40 ; n <- 1200
dat <- huge.generator(n, p, "hub", verbose=FALSE, v=.1, u=.3)
lams <- getLamPath(getMaxCov(dat$data), .01, len=20)
## Run pulsar with huge
hugeargs <- list(lambda=lams, verbose=FALSE)
out.p <- pulsar(dat$data, fun=huge::huge, fargs=hugeargs,
rep.num=20, criterion='stars')
## Run pulsar in bounded stars mode and include gcd metric:
out.b <- pulsar(dat$data, fun=huge::huge, fargs=hugeargs,
rep.num=20, criterion=c('stars', 'gcd'),
lb.stars=TRUE, ub.stars=TRUE)
plot(out.b)
## End(Not run)
``` |

pulsar documentation built on Sept. 2, 2018, 9:03 a.m.

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