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R/AIC_deprecated.R
In spmodel: Spatial Statistical Modeling and Prediction
# #' Compute AIC and AICc of fitted model objects
# #'
# #' @description Compute AIC and AICc for one or
# #' several fitted model objects for which a log-likelihood
# #' value can be obtained.
# #'
# #' @param object A fitted model object from [splm()], [spautor()], [spglm()], or [spgautor()]
# #' where \code{estmethod} is \code{"ml"} or \code{"reml"}.
# #' @param ... Optionally more fitted model objects.
# #' @param k The penalty parameter, taken to be 2. Currently not allowed to differ
# #' from 2 (needed for generic consistency).
# #'
# #' @details When comparing models fit by maximum or restricted maximum
# #' likelihood, the smaller the AIC or AICc, the better the fit. The AICc contains
# #' a correction to AIC for small sample sizes. The theory of
# #' AIC and AICc requires that the log-likelihood has been maximized, and hence,
# #' no AIC or AICc methods exist for models where \code{estmethod} is not
# #' \code{"ml"} or \code{"reml"}. Additionally, AIC and AICc comparisons between \code{"ml"}
# #' and \code{"reml"} models are meaningless -- comparisons should only be made
# #' within a set of models estimated using \code{"ml"} or a set of models estimated
# #' using \code{"reml"}. AIC and AICc comparisons for \code{"reml"} must
# #' use the same fixed effects. To vary the covariance parameters and
# #' fixed effects simultaneously, use \code{"ml"}.
# #'
# #' Hoeting et al. (2006) defines that spatial AIC as
# #' \eqn{-2loglik + 2(estparams)} and the spatial AICc as
# #' \eqn{-2loglik + 2n(estparams) / (n - estparams - 1)}, where \eqn{n} is the sample size
# #' and \eqn{estparams} is the number of estimated parameters. For \code{"ml"}, \eqn{estparams} is
# #' the number of estimated covariance parameters plus the number of estimated
# #' fixed effects. For \code{"reml"}, \eqn{estparams} is the number of estimated covariance
# #' parameters.
# #'
# #' @return If just one object is provided, a numeric value with the corresponding
# #' AIC or AICc.
# #'
# #' If multiple objects are provided, a \code{data.frame} with rows corresponding
# #' to the objects and columns representing the number of parameters estimated
# #' (\code{df}) and the AIC or AICc.
# #'
# #' @name AIC.spmodel
# #' @method AIC splm
# #' @order 1
# #' @export
# #'
# #' @examples
# #' spmod <- splm(z ~ water + tarp,
# #' data = caribou,
# #' spcov_type = "exponential", xcoord = x, ycoord = y
# #' )
# #' AIC(spmod)
# #' AICc(spmod)
# AIC.splm <- function(object, ..., k = 2) {
#
# # set k as 2
# k <- 2
#
# # store object and ...
# object_list <- list(object, ...)
#
# # see if ... has any elements
# if (length(object_list) == 1) {
#
# # number of estimated parameters
# if (object$estmethod == "ml") {
# n_est_param <- object$npar + object$p
# } else {
# n_est_param <- object$npar
# }
#
# # error if not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AIC is only defined if estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
# # compute AIC
# AIC_val <- -2 * logLik(object) + k * (n_est_param)
# } else {
#
#
# # warning if ml and reml in same call
# est_methods <- vapply(object_list, function(x) x$estmethod, character(1))
# if ("ml" %in% est_methods && "reml" %in% est_methods) {
# warning("AIC and AICc should not compare models fit with
# \"ml\" to models fit with \"reml\"", call. = FALSE)
# }
#
# # warning if reml and fixed effects change
# est_methods_reml <- which(est_methods == "reml")
# if (length(est_methods_reml) > 1) {
# if (any(vapply(
# est_methods_reml,
# function(x) !identical(formula(object_list[[x]]), formula(object_list[[1]])), logical(1)
# ))) {
# warning("AIC and AICc should not be used to compare models fit with \"reml\" whose fixed effect formulas differ.", call. = FALSE)
# }
# }
#
# # find model names provided
# object_list_names <- as.character(c(substitute(object), (as.list(substitute(list(...)))[-1])))
#
# # error if any names duplicated
# if (any(duplicated(object_list_names))) {
# stop("Each model object must have a unique name", call. = FALSE)
# }
# # iterate through each model
# object_AIC <- lapply(object_list, function(x) {
# # warning if estmethod not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AIC is only defined is estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
#
# if (x$estmethod == "ml") {
# n_est_param <- x$npar + x$p
# } else {
# n_est_param <- x$npar
# }
#
# # store degrees of freedom (parames estimated) and AIC
# data.frame(df = n_est_param, AIC = -2 * logLik(x) + k * (n_est_param))
# })
# # put all AIC data frames together
# AIC_val <- do.call("rbind", object_AIC)
# # set rownames as model names
# row.names(AIC_val) <- object_list_names
# }
# # return AIC value
# AIC_val
# }
#
# #' @rdname AIC.spmodel
# #' @method AIC spautor
# #' @order 2
# #' @export
# AIC.spautor <- AIC.splm
#
# #' @rdname AIC.spmodel
# #' @order 5
# #' @export
# AICc <- function(object, ..., k = 2) {
# # method dispatch
# UseMethod("AICc", object)
# }
#
# #' @rdname AIC.spmodel
# #' @method AICc splm
# #' @order 6
# #' @export
# AICc.splm <- function(object, ..., k = 2) {
#
# # set k as 2
# k <- 2
#
# # store object and ...
# object_list <- list(object, ...)
#
# # see if ... has any elements
# if (length(object_list) == 1) {
#
# # number of estimated parameters
# if (object$estmethod == "ml") {
# n_est_param <- object$npar + object$p
# } else {
# n_est_param <- object$npar
# }
#
# # error if not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AICc is only defined if estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
# # compute AICc
# AICc_val <- -2 * logLik(object) + 2 * object$n * (n_est_param) / (object$n - n_est_param - 1)
# } else {
#
# # warning if ml and reml in same call
# est_methods <- vapply(object_list, function(x) x$estmethod, character(1))
# if ("ml" %in% est_methods && "reml" %in% est_methods) {
# warning("AICc and AICcc should not compare models fit with
# \"ml\" to models fit with \"reml\"", call. = FALSE)
# }
#
# # warning if reml and fixed effects change
# est_methods_reml <- which(est_methods == "reml")
# if (length(est_methods_reml) > 1) {
# if (any(vapply(
# est_methods_reml,
# function(x) !identical(formula(object_list[[x]]), formula(object_list[[1]])), logical(1)
# ))) {
# warning("AIC and AICc should not be used to compare models fit with \"reml\" whose fixed effect formulas differ.", call. = FALSE)
# }
# }
#
# # find model names provided
# object_list_names <- as.character(c(substitute(object), (as.list(substitute(list(...)))[-1])))
#
# # error if any names duplicated
# if (any(duplicated(object_list_names))) {
# stop("Each model object must have a unique name", call. = FALSE)
# }
# # iterate through each model
# object_AICc <- lapply(object_list, function(x) {
# # warning if estmethod not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AICc is only defined is estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
#
# if (x$estmethod == "ml") {
# n_est_param <- x$npar + x$p
# } else {
# n_est_param <- x$npar
# }
#
# # store degrees of freedom (parames estimated) and AICc
# data.frame(df = n_est_param, AICc = -2 * logLik(x) + 2 * x$n * (n_est_param) / (x$n - n_est_param - 1))
# })
# # put all AICc data frames together
# AICc_val <- do.call("rbind", object_AICc)
# # set rownames as model names
# row.names(AICc_val) <- object_list_names
# }
# # return AICc value
# AICc_val
# }
#
# #' @rdname AIC.spmodel
# #' @method AICc spautor
# #' @order 7
# #' @export
# AICc.spautor <- AICc.splm
#
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# #' Compute AIC and AICc of fitted model objects
# #'
# #' @description Compute AIC and AICc for one or
# #' several fitted model objects for which a log-likelihood
# #' value can be obtained.
# #'
# #' @param object A fitted model object from [splm()], [spautor()], [spglm()], or [spgautor()]
# #' where \code{estmethod} is \code{"ml"} or \code{"reml"}.
# #' @param ... Optionally more fitted model objects.
# #' @param k The penalty parameter, taken to be 2. Currently not allowed to differ
# #' from 2 (needed for generic consistency).
# #'
# #' @details When comparing models fit by maximum or restricted maximum
# #' likelihood, the smaller the AIC or AICc, the better the fit. The AICc contains
# #' a correction to AIC for small sample sizes. The theory of
# #' AIC and AICc requires that the log-likelihood has been maximized, and hence,
# #' no AIC or AICc methods exist for models where \code{estmethod} is not
# #' \code{"ml"} or \code{"reml"}. Additionally, AIC and AICc comparisons between \code{"ml"}
# #' and \code{"reml"} models are meaningless -- comparisons should only be made
# #' within a set of models estimated using \code{"ml"} or a set of models estimated
# #' using \code{"reml"}. AIC and AICc comparisons for \code{"reml"} must
# #' use the same fixed effects. To vary the covariance parameters and
# #' fixed effects simultaneously, use \code{"ml"}.
# #'
# #' Hoeting et al. (2006) defines that spatial AIC as
# #' \eqn{-2loglik + 2(estparams)} and the spatial AICc as
# #' \eqn{-2loglik + 2n(estparams) / (n - estparams - 1)}, where \eqn{n} is the sample size
# #' and \eqn{estparams} is the number of estimated parameters. For \code{"ml"}, \eqn{estparams} is
# #' the number of estimated covariance parameters plus the number of estimated
# #' fixed effects. For \code{"reml"}, \eqn{estparams} is the number of estimated covariance
# #' parameters.
# #'
# #' @return If just one object is provided, a numeric value with the corresponding
# #' AIC or AICc.
# #'
# #' If multiple objects are provided, a \code{data.frame} with rows corresponding
# #' to the objects and columns representing the number of parameters estimated
# #' (\code{df}) and the AIC or AICc.
# #'
# #' @name AIC.spmodel
# #' @method AIC splm
# #' @order 1
# #' @export
# #'
# #' @examples
# #' spmod <- splm(z ~ water + tarp,
# #' data = caribou,
# #' spcov_type = "exponential", xcoord = x, ycoord = y
# #' )
# #' AIC(spmod)
# #' AICc(spmod)
# AIC.splm <- function(object, ..., k = 2) {
#
# # set k as 2
# k <- 2
#
# # store object and ...
# object_list <- list(object, ...)
#
# # see if ... has any elements
# if (length(object_list) == 1) {
#
# # number of estimated parameters
# if (object$estmethod == "ml") {
# n_est_param <- object$npar + object$p
# } else {
# n_est_param <- object$npar
# }
#
# # error if not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AIC is only defined if estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
# # compute AIC
# AIC_val <- -2 * logLik(object) + k * (n_est_param)
# } else {
#
#
# # warning if ml and reml in same call
# est_methods <- vapply(object_list, function(x) x$estmethod, character(1))
# if ("ml" %in% est_methods && "reml" %in% est_methods) {
# warning("AIC and AICc should not compare models fit with
# \"ml\" to models fit with \"reml\"", call. = FALSE)
# }
#
# # warning if reml and fixed effects change
# est_methods_reml <- which(est_methods == "reml")
# if (length(est_methods_reml) > 1) {
# if (any(vapply(
# est_methods_reml,
# function(x) !identical(formula(object_list[[x]]), formula(object_list[[1]])), logical(1)
# ))) {
# warning("AIC and AICc should not be used to compare models fit with \"reml\" whose fixed effect formulas differ.", call. = FALSE)
# }
# }
#
# # find model names provided
# object_list_names <- as.character(c(substitute(object), (as.list(substitute(list(...)))[-1])))
#
# # error if any names duplicated
# if (any(duplicated(object_list_names))) {
# stop("Each model object must have a unique name", call. = FALSE)
# }
# # iterate through each model
# object_AIC <- lapply(object_list, function(x) {
# # warning if estmethod not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AIC is only defined is estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
#
# if (x$estmethod == "ml") {
# n_est_param <- x$npar + x$p
# } else {
# n_est_param <- x$npar
# }
#
# # store degrees of freedom (parames estimated) and AIC
# data.frame(df = n_est_param, AIC = -2 * logLik(x) + k * (n_est_param))
# })
# # put all AIC data frames together
# AIC_val <- do.call("rbind", object_AIC)
# # set rownames as model names
# row.names(AIC_val) <- object_list_names
# }
# # return AIC value
# AIC_val
# }
#
# #' @rdname AIC.spmodel
# #' @method AIC spautor
# #' @order 2
# #' @export
# AIC.spautor <- AIC.splm
#
# #' @rdname AIC.spmodel
# #' @order 5
# #' @export
# AICc <- function(object, ..., k = 2) {
# # method dispatch
# UseMethod("AICc", object)
# }
#
# #' @rdname AIC.spmodel
# #' @method AICc splm
# #' @order 6
# #' @export
# AICc.splm <- function(object, ..., k = 2) {
#
# # set k as 2
# k <- 2
#
# # store object and ...
# object_list <- list(object, ...)
#
# # see if ... has any elements
# if (length(object_list) == 1) {
#
# # number of estimated parameters
# if (object$estmethod == "ml") {
# n_est_param <- object$npar + object$p
# } else {
# n_est_param <- object$npar
# }
#
# # error if not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AICc is only defined if estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
# # compute AICc
# AICc_val <- -2 * logLik(object) + 2 * object$n * (n_est_param) / (object$n - n_est_param - 1)
# } else {
#
# # warning if ml and reml in same call
# est_methods <- vapply(object_list, function(x) x$estmethod, character(1))
# if ("ml" %in% est_methods && "reml" %in% est_methods) {
# warning("AICc and AICcc should not compare models fit with
# \"ml\" to models fit with \"reml\"", call. = FALSE)
# }
#
# # warning if reml and fixed effects change
# est_methods_reml <- which(est_methods == "reml")
# if (length(est_methods_reml) > 1) {
# if (any(vapply(
# est_methods_reml,
# function(x) !identical(formula(object_list[[x]]), formula(object_list[[1]])), logical(1)
# ))) {
# warning("AIC and AICc should not be used to compare models fit with \"reml\" whose fixed effect formulas differ.", call. = FALSE)
# }
# }
#
# # find model names provided
# object_list_names <- as.character(c(substitute(object), (as.list(substitute(list(...)))[-1])))
#
# # error if any names duplicated
# if (any(duplicated(object_list_names))) {
# stop("Each model object must have a unique name", call. = FALSE)
# }
# # iterate through each model
# object_AICc <- lapply(object_list, function(x) {
# # warning if estmethod not ml or reml
# if (!object$estmethod %in% c("ml", "reml")) {
# stop("AICc is only defined is estmethod is \"ml\" or \"reml\".", call. = FALSE)
# }
#
# if (x$estmethod == "ml") {
# n_est_param <- x$npar + x$p
# } else {
# n_est_param <- x$npar
# }
#
# # store degrees of freedom (parames estimated) and AICc
# data.frame(df = n_est_param, AICc = -2 * logLik(x) + 2 * x$n * (n_est_param) / (x$n - n_est_param - 1))
# })
# # put all AICc data frames together
# AICc_val <- do.call("rbind", object_AICc)
# # set rownames as model names
# row.names(AICc_val) <- object_list_names
# }
# # return AICc value
# AICc_val
# }
#
# #' @rdname AIC.spmodel
# #' @method AICc spautor
# #' @order 7
# #' @export
# AICc.spautor <- AICc.splm
#
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