IBIC: Information Matrix-Based Information Criterion

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Information Matrix-Based Information Criterion (IBIC) for "lm" and "glm" objects.

Usage

IBIC(model)

Arguments

model

a "lm" or "glm" object

Details

IBIC (Bollen et al., 2012) is calculated as

-2LL(theta) + klog(n/(2pi)) + log(|F|)

F is the fisher information matrix.

While calculating the Fisher information matrix (F), we used the joint parameters (beta,sigma^2) of the models.

Value

IBIC measurement of the model

References

Bollen, K. A., Ray, S., Zavisca, J., & Harden, J. J. (2012). A comparison of Bayes factor approximation methods including two new methods. Sociological Methods & Research, 41(2), 294-324.

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")

IBIC(m1)
IBIC(m2)
IBIC(m3)

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