coef.ggmncv: Regression Coefficients from 'ggmncv' Objects

In GGMncv: Gaussian Graphical Models with Nonconvex Regularization

Description

There is a direct correspondence between the inverse covariance matrix and multiple regression \insertCitestephens1998,kwan2014regressionGGMncv. This readily allows for converting the off diagonal elements to regression coefficients, resulting in noncovex penalization for multiple regression modeling.

Usage

## S3 method for class 'ggmncv'
coef(object, ...)

Arguments

object	An Object of class ggmncv.
...	Currently ignored.

Value

A matrix of regression coefficients.

Note

The coefficients can be accessed via coefs[1,], which provides the estimates for predicting the first node.

Further, the estimates are essentially computed with both the outcome and predictors scaled to have mean 0 and standard deviation 1.

References

\insertAllCited

Examples

# data
Y <- GGMncv::ptsd[,1:5]

# correlations
S <- cor(Y)

# fit model
fit <- ggmncv(R = S, n = nrow(Y), progress = FALSE)

# regression
coefs <- coef(fit)

coefs


# no regularization, resulting in OLS

# data
# note: scaled for lm()
Y <- scale(GGMncv::ptsd[,1:5])

# correlations
S <- cor(Y)

# fit model
# note: non reg
fit <- ggmncv(R = S, n = nrow(Y), progress = FALSE, lambda = 0)

# regression
coefs <- coef(fit)

# fit lm
fit_lm <- lm(Y[,1] ~ 0 + Y[,-1])

# ggmncv
coefs[1,]

# lm
as.numeric(coef(fit_lm))

GGMncv documentation built on Dec. 15, 2021, 9:10 a.m.