inet (version 0.1.0)

OLS: 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.

References

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

Examples

# NOT RUN {
# 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)

# }
# NOT RUN {
# }