KIC: Kullback–Leibler Information Criterion

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Kullback–Leibler Information Criterion (KIC) and its corrected form (KICC) for "lm" and "glm" objects.

Usage

KIC(model)

KICC(model)

Arguments

model

a "lm" or "glm" object

Details

KIC (Seghouane, 2006) is calculated as

-2LL(theta) + 3k

and KICC (Seghouane, 2006) is calculated as

-2LL(theta) + ((k + 1)(3n - k - 2)) + (k/(n-k))

Value

KIC measurement of the model

References

Seghouane, A. K. (2006). A note on overfitting properties of KIC and KICC. Signal Processing, 86(10), 3055-3060.

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")

KIC(m1)
KIC(m2)
KIC(m3)
KICC(m1)
KICC(m2)
KICC(m3)

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