View source: R/mcPulsarSelect.R
pulsar | R Documentation |
Run pulsar using StARS' edge stability (or other criteria) to select an undirected graphical model over a lambda path.
pulsar(
data,
fun = huge::huge,
fargs = list(),
criterion = c("stars"),
thresh = 0.1,
subsample.ratio = NULL,
rep.num = 20,
seed = NULL,
lb.stars = FALSE,
ub.stars = FALSE,
ncores = 1,
refit = TRUE
)
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 lambda
criterion: the stability criterion used
merge: the raw statistic over the rep.num
graphs, prior to summarization
opt.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 TRUE
ub.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. https://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.
batch.pulsar
refit
## 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)
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