lasso_ms: Estimate GMM with inference via the multi-split method.

In inet: Performing Inference on Networks with Regularization

Estimate GMM with inference via the multi-split method.

Description

Estimate Gaussian Graphical Models with inference base don the multi-split method. This is a wrapper of the function multi.split of the hdi package.

Usage

lasso_ms(data, B = 50, fraction = 0.5, 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
B	The number of sample-splits. Defaults to B=50.
fraction	a number in (0,1), the fraction of data used at each sample split for the model selection process. The remaining data is used for calculating the p-values.
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..

Wasserman, L., & Roeder, K. (2009). High dimensional variable selection. Annals of statistics, 37(5A), 2178.

Meinshausen, N., Meier, L., & Bühlmann, P. (2009). P-values for high-dimensional regression. Journal of the American Statistical Association, 104(488), 1671-1681.

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_ms(data = data, B=2)
# !!! NOTE: this is just for testing purposes; B should a lot higher (default = 50)

## Not run: 

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


## End(Not run)

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