lasso: Estimate GGM with nodewise regression and the lasso.

In inet: Performing Inference on Networks with Regularization

Estimate GGM with nodewise regression and the lasso.

Description

Estimate a Gaussian Graphical Model with lasso-regularized nodewise regression, where the regularization parameter is selected with cross-validation. This is a wrapper around the function cv.glmnet() from the glmnet package.

Usage

lasso(data, pbar = TRUE, nfolds = 10, rulereg = "and")

Arguments

data	An n x p matrix containing the data, where n are cases and p are variables
pbar	If pbar = TRUE, a progress bar will be displayed.
nfolds	Specifies the number of folds used to select the regularization parameter in each of the p nodewise regressions.
rulereg	Specifies how parameter estimates should be combined across nodewise regressions. The options are the AND-rule (requiring both estimates to be significant) or the OR-rule (only requiring one estimate to be significant). Defaults to rulereg = "and".

Value

The function returns a list with the following entries:

est	A p x p matrix with point estimates for all partial correlations
select	A p x p indicator matrix indicating which edges have been selected to be present.
ints	A p-vector of estimated intercepts.

Author(s)

Jonas Haslbeck <[e-mail]>

References

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.

Examples

# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
set.seed(1)
out <- lasso(data = data)

## Not run: 

# Fit GGM to PTSD data
set.seed(1)
out <- lasso(data = ptsd_data)


## End(Not run)

inet documentation built on March 18, 2022, 5:41 p.m.