AIC: Akaike Information Criterion

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Akaike Information Criterion (AIC) and its variants for "lm" and "glm" objects.

Usage

AIC(model)

AIC4(model)

Arguments

model

a "lm" or "glm" object

Details

AIC (Akaike, 1973) is calculated as

-2LL(theta) + 2k

and AIC4 (Bozdogan, 1994) as

-2LL(theta) + 2klog

Value

AIC or AIC4 measurement of the model

References

Akaike H., 1973. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika, 60(2), 255-265.

Bozdogan, H. 1994. Mixture-model cluster analysis using model selection criteria and a new informational measure of complexity. In Proceedings of the first US/Japan conference on the frontiers of statistical modeling: An informational approach, 69–113. Dordrecht: Springer.

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")

AIC(m1)
AIC(m2)
AIC(m3)
AIC4(m1)
AIC4(m2)
AIC4(m3)

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