FIC: Fisher Information Criterion

In ICglm: Information Criteria for Generalized Linear Regression

Description

Calculates Fisher Information Criterion (FIC) for "lm" and "glm" objects.

Usage

FIC(model)

Arguments

model

a "lm" or "glm" object

Details

FIC (Wei, 1992) is calculated as

-2LL(theta) + log(|X^T X|)

Value

FIC measurement of the model

References

Wei, C. Z. (1992). On predictive least squares principles. The Annals of Statistics, 20(1), 1-42.

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")

FIC(m1)
FIC(m2)
FIC(m3)

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