CAIC {ICglm} | R Documentation |
Consistent Akaike's Information Criterion (CAIC) and Consistent Akaike's Information Criterion with Fisher Information (CAICF) for "lm" and "glm" objects.
CAIC(model)
CAICF(model)
model |
a "lm" or "glm" object. |
CAIC (Bozdogan, 1987) is calculated as
-2LL(theta) + k(log(n) + 1)
CAICF (Bozdogan, 1987) as
-2LL(theta) + 2k + k(log(n)) + log(|F|)
F is the Fisher information matrix.
CAIC or CAICF measurement of the model.
Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
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")
CAIC(m1)
CAIC(m2)
CAIC(m3)
CAICF(m1)
CAICF(m2)
CAICF(m3)