lasso_dsp {inet}

Estimate GGMs with the desparsified lasso.

Description

Estimate Gaussian Graphical Models using the desparsified lasso. This is a wrapper around the function lasso.proj of the hdi package.

Usage

lasso_dsp(data, betainit = "cv lasso", ci.level = 0.95,
          correction = TRUE, pbar = TRUE, rulereg = "and")

Arguments

data

An n x p matrix containing the data, where n are cases and p are variables

betainit

Specifying how to estimate lasso solution in initial estimation. Either betainit = "cv lasso" (default) or betainit = "cv lasso". See the manual of the function lasso.proj of the hdi package for more info.

ci.level

Specifies the width of the confidence interval used for testing the null hypothesis that a parameter is different to zero. Defaults to ci.level = 0.95, which corresponds to a critical threshold of 0.05.

correction

If correction = TRUE, the Bonferroni-Holm correction will be applied to p-values on the level of nodewise regressions (see e.g., Hochberg, 1987).

pbar

If pbar = TRUE, a progress bar will be displayed.

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

est.signf

A p x p matrix with point estimates for all partial correlations with non-significant partial correlations being thresholded to zero.

signf

A p x p matrix indicating for each partial correlation whether it is significantly different to zero.

ci.lower

A p x p matrix indicating the lower confidence interval for each partial correlation.

ci.upper

A p x p matrix indicating the upper confidence interval for each partial correlation.

Author(s)

Jonas Haslbeck <[e-mail]>; Lourens Waldorp <[e-mail]>

References

Hochberg, Y., & Tamhane, A. C. (1987). Multiple comparison procedures. John Wiley & Sons, Inc..

Buehlmann, P., Kalisch, M., & Meier, L. (2014). High-dimensional statistics with a view toward applications in biology. Annual Review of Statistics and Its Application, 1, 255-278.

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_dsp(data = data)

## Not run: 

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


## End(Not run)