#' Return table comparing conditional AIC (cAIC) values for multiple models produced using SAS GLIMMIX.
#'
#' @param y
#'
#' @export
cAIC_function <- function(y) {
y %<>%
mutate(
cAIC = `-2 log L(FruitPres_t | r. effects)` +
2*(`Upper Bound, Number of Parameters` + ColumnsZ)
)
`min(cAIC)` = min(y$cAIC)
y %<>%
mutate(
`delta cAIC` = cAIC - `min(cAIC)`,
`Model Lik` = exp((-1/2)*`delta cAIC`)
)
sum.L = sum(y$`Model Lik`)
y %<>% mutate(`Prob(Model)` = `Model Lik`/sum.L)
y$`Prob(Model)` %<>% round(digits=2)
y$`Model Lik` %<>% round(digits=2)
y %<>% dplyr::select(-c(
cAIC,
`Model Lik`,
`-2 log L(FruitPres_t | r. effects)`,
ColumnsZ
)) %>%
arrange(`delta cAIC`)
return(y)
}
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