extractAIC.glm.Burnham.Anderson <- function(fit, scale = 0, k = 2, ...)
{
# *** Gives AIC defined in Burnham and Anderson page 61 - not the same as R ***
# *** Only for Gaussian ***
n <- length(fit$residuals)
edf <- n - fit$df.residual + 1
dev <- fit$deviance
if(scale > 0)
dev <- dev/scale
if(scale == 0 && fit$family$family == "gaussian") {
cat("\nGausssian with scale = 0.0\n")
cat("\nLog likelihood = ", (-n/2) * log(dev/n) , "\n\n")
dev <- n * log(dev/n)
}
c(edf, dev + k * edf)
}
extractAIC.c.Burnham.Anderson.glm <-
# *** Gives AIC defined in Burnham and Anderson page 61 - not the same as R ***
# *** Only for Gaussian ***
function(fit, scale = 0, k = 2, ...) {
n <- length(fit$residuals)
edf <- n - fit$df.residual + 1
dev <- fit$deviance
if(scale > 0)
dev <- dev/scale
if(scale == 0 && fit$family$family == "gaussian")
dev <- n * log(dev/n)
c(edf, dev + k * edf + (2 * edf * (edf + 1))/(n - edf - 1))
}
extractQAIC.Burnham.Anderson.glm <-
function(fit, scale = 0, k = 2, ...) {
# *** Gives AIC defined in Burnham and Anderson page 61 - not the same as R ***
# *** Only for Gaussian ***
n <- length(fit$residuals)
edf <- n - fit$df.residual + 2 # One extra for estimating dispersion
dev <- fit$deviance
if(scale > 0)
dev <- dev/scale
if(scale == 0 && fit$family$family == "gaussian")
dev <- n * log(dev/n)
c(edf, dev + k * edf)
}
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