HBIC: Haughton Bayesian information criterion

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Haughton Bayesian information criterion (HBIC) for "lm" and "glm" objects.

Usage

HBIC(model)

Arguments

model

a "lm" or "glm" object

Details

HBIC (Bollen et al., 2014) is calculated as

-2LL(theta) + klog(n/(2pi))

Value

HBIC measurement of the model

References

Bollen, K. A., Harden, J. J., Ray, S., & Zavisca, J. (2014). BIC and alternative Bayesian information criteria in the selection of structural equation models. Structural equation modeling: a multidisciplinary journal, 21(1), 1-19.

Examples

x1 <- rnorm(100, 3, 2)
x2 <- rnorm(100, 5, 3)
x3 <- rnorm(100, 67, 5)
err <- rnorm(100, 0, 4)

## round so we can use it for Poisson regression
y <- round(3 + 2*x1 - 5*x2 + 8*x3 + err)

m1 <- lm(y~x1 + x2 + x3)
m2 <- glm(y~x1 + x2 + x3, family = "gaussian")
m3 <- glm(y~x1 + x2 + x3, family = "poisson")

HBIC(m1)
HBIC(m2)
HBIC(m3)

ICglm documentation built on Nov. 12, 2021, 1:06 a.m.