SPBIC: Scaled Unit Information Prior Bayesian Information Criterion

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Scaled Unit Information Prior Bayesian Information Criterion (SPBIC) for "lm" and "glm" objects.

Usage

SPBIC(model)

Arguments

model

a "lm" or "glm" object

Details

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

-2LL(theta) + k(1 - log(k/(beta^T(Sigma)^{-1}beta)))

beta and Sigma are vector and covariance matrix of regression coefficients.

Value

SPBIC 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")

SPBIC(m1)
SPBIC(m2)
SPBIC(m3)

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