https://github.com/cran/MuMIn
Tip revision: 69fda28b879d6147f7dda937d28114b28cc8ebc8 authored by Kamil BartoĊ on 14 May 2014, 02:44:24 UTC
version 1.10.0
version 1.10.0
Tip revision: 69fda28
QAIC.R
# AIC = -2 * LL + 2 * n
# LL = -1/2 * log(dev) * n + C
# AIC = log(dev) * n + C + 2 * n
`QAIC` <-
function(object, ..., chat, k = 2) {
#chat <- summary(gm)$dispersion
# chat <- deviance(object) / df.residual(object)
logLik <- .getLogLik()
if(chat < 1) {
warning("'chat' given is < 1, increased to 1")
chat <- 1
}
npar.adj <- if(chat == 1) 0 else 1
qaic <- function(object, chat, k) {
ll <- logLik(object)
# no <- attr(ll, "nobs")
# df is the number of parameters plus 1 for estimating c-hat
df <- attr(ll, "df") + npar.adj
#neg2ll <- log(deviance(object)) * no # + Constant...
neg2ll <- -2 * c(ll)
#ret <- (neg2ll * no / chat) + k * df
#ret <- -2 * ll / chat + k * df
ret <- neg2ll / chat + k * df
return (ret)
}
if(length(list(...))) {
object <- list(object, ...)
val <- data.frame(QAIC = sapply(object, qaic, chat = chat, k = k))
Call <- match.call()
Call$chat <- NULL
row.names(val) <- as.character(Call[-1L])
return(val)
} else {
return(qaic(object, chat, k))
}
}
`QAICc` <-
function(object, ..., chat, k = 2) {
logLik <- .getLogLik()
if(chat < 1) {
warning("'chat' given is < 1, increased to 1")
chat <- 1
}
npar.adj <- if(chat == 1) 0 else 1
qaicc <- function(object, chat, k) {
ll <- logLik(object)
no <- .get.nobs(ll, error = FALSE)
if (is.null(no)) no <- nobs(object, use.fallback = FALSE)
# df is the number of parameters plus 1 for estimating c-hat
df <- attr(ll, "df") + npar.adj
#neg2ll <- log(deviance(object)) * n # + Constant...
neg2ll <- -2 * c(ll) # + Constant...
ret <- (neg2ll / chat) + (k * df) * (1 + ((df + 1) / (no - df - 1)))
return (ret)
}
if(length(list(...))) {
object <- list(object, ...)
val <- data.frame(QAIC = sapply(object, qaicc, chat = chat, k = k))
Call <- match.call()
Call$chat <- NULL
row.names(val) <- as.character(Call[-1L])
return(val)
} else {
return(qaicc(object, chat, k))
}
}
#QAIC = -2log Likelihood/c-hat + 2K
#QAICc = -2log Likelihood/c-hat + 2K + 2K(K + 1)/(n - K - 1)