cv.MNS: Select regularization parameters via cross-validation

In MNS: Mixed Neighbourhood Selection

Description

Select regularization parameters via K-fold cross-validation

Usage

cv.MNS(dat, l1range, alpharange, 
    K = 5, parallel = FALSE, 
    cores = NULL, verbose = FALSE)

Arguments

dat	List where each entry corresponds to the time series observations for each subject
l1range	Vector of candidate regularization parameters. See details below
alpharange	Vector of candidate weighting parameters. See details below.
K	Number of cross-validation folds
parallel	Indicate whether model fit should be done in parallel. Default is FALSE
cores	If fit in parallel, indicate how many units/cores should be used
verbose	Print progress. Only available for non-parallel implementation

Details

Select regularization parameters via cross-validation. In the interest of simplicity we re-parameterize penalty as an elastic net penalty:

λ * α || β||_1 + λ * (1-α) || σ||_1

Thus λ is the regularization parameter (specified by the l1range argument) and α is the weighting parameter (specified by the alpharange argument).

Value

l1	selected regularization parameter
alpha	selected weighting parameter
CV	grid of cross-validation error for each pair of regularization parameters

Author(s)

Ricardo Pio Monti

References

Arlot, S., and Alain C. "A survey of cross-validation procedures for model selection." Statistics surveys 4 (2010): 40-79.

Monti, R., Anagnostopolus, C., Montana, G. "Inferring brain connectivity networks from functional MRI data via mixed neighbourhood selection", arXiv, 2015

See Also

MNS

Examples

set.seed(1)
Dat = gen.Network(p = 10, Nsub = 5, 
 sparsity = .2, REsize=10, REprob=.5, 
 REnoise = 1, Nobs=20)
## Not run: 
CVs = cv.MNS(dat = Dat, l1range = seq(.1, .5, length.out=10),
 alpharange = seq(.2, .8, length.out = 5), 
 parallel = FALSE, verbose = TRUE)

## End(Not run)

MNS documentation built on May 2, 2019, 9:33 a.m.