OLS: Estimate GMM via nodewise regression and hypothesis tests.

In inet: Performing Inference on Networks with Regularization

Estimate GMM via nodewise regression and hypothesis tests.

Description

Estimate Gaussian Graphical Model with nodewise regression, selecting edges with standard hypothesis tests and the Bonferroni-Holm Correction.

Usage

OLS(data, pbar = TRUE, correction = TRUE,
    ci.level = 0.95, 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.
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).
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.
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.
ints	A p-vector of estimated intercepts.

Author(s)

Jonas Haslbeck <[e-mail]>

References

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

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))
out <- OLS(data = data)

## Not run: 

# Fit GGM to PTSD data
out <- OLS(data = ptsd_data)


## End(Not run)

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