-
Notifications
You must be signed in to change notification settings - Fork 27
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- style code - copy edit docs - define cAIC generic - CAIC -> cAIC - add example - add unit test - don't require() Matrix
- Loading branch information
1 parent
52a0eef
commit 5e1cda8
Showing
5 changed files
with
188 additions
and
127 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,110 +1,129 @@ | ||
|
||
#' @title Calculate conditional AIC | ||
#' Calculate conditional AIC | ||
#' | ||
#' @description | ||
#' Calculates the conditional Akaike Information criterion (cAIC). | ||
#' | ||
#' @param object Output from \code{\link{sdmTMB}} | ||
#' @param object Output from [sdmTMB()]. | ||
#' @param what Whether to return the cAIC or the effective degrees of freedom | ||
#' (EDF) for each group of random effects. | ||
#' (EDF) for each group of random effects. | ||
#' @param ... Other arguments for specific methods. Not used. | ||
#' | ||
#' @details cAIC is designed to optimize the expected out-of-sample predictive | ||
#' performance for new data that share the same random effects as the in-sample | ||
#' (fitted) data, e.g., spatial interpolation. In this sense, it should be a | ||
#' fast approximation to optimizing the model structure based on k-fold | ||
#' cross-validation. | ||
#' | ||
#' @details | ||
#' cAIC is designed to optimize the expected out-of-sample predictive | ||
#' performance for new data that share the same random effects as the | ||
#' in-sample (fitted) data, e.g., spatial interpolation. In this sense, | ||
#' it should be a fast approximation to optimizing the model structure | ||
#' based on k-fold crossvalidation. | ||
#' By contrast, \code{AIC} calculates the | ||
#' marginal Akaike Information Criterion, which is designed to optimize | ||
#' expected predictive performance for new data that have new random effects, | ||
#' e.g., extrapolation, or inference about generative parameters. | ||
#' By contrast, [AIC()] calculates the marginal Akaike Information Criterion, | ||
#' which is designed to optimize expected predictive performance for new data | ||
#' that have new random effects, e.g., extrapolation, or inference about | ||
#' generative parameters. | ||
#' | ||
#' cAIC also calculates as a byproduct the effective degrees of freedom, | ||
#' i.e., the number of fixed effects that would have an equivalent impact on | ||
#' cAIC also calculates the effective degrees of freedom (EDF) as a byproduct. | ||
#' This is the number of fixed effects that would have an equivalent impact on | ||
#' model flexibility as a given random effect. | ||
#' | ||
#' Both cAIC and EDF are calculated using Eq. 6 of Zheng Cadigan Thorson 2024. | ||
#' Both cAIC and EDF are calculated using Eq. 6 of Zheng, Cadigan, and Thorson | ||
#' (2024). | ||
#' | ||
#' Note that, for models that include profiled fixed effects, these profiles | ||
#' are turned off. | ||
#' For models that include profiled fixed effects, these profiles are turned | ||
#' off. | ||
#' | ||
#' @return | ||
#' Either the cAIC, or the effective degrees of freedom (EDF) by group | ||
#' of random effects | ||
#' Either the cAIC or the effective degrees of freedom (EDF) by group | ||
#' of random effects depending on the argument `what`. | ||
#' | ||
#' @references | ||
#' | ||
#' **Deriving the general approximation to cAIC used here** | ||
#' **Deriving the general approximation to cAIC used here:** | ||
#' | ||
#' Zheng, N., Cadigan, N., & Thorson, J. T. (2024). | ||
#' A note on numerical evaluation of conditional Akaike information for | ||
#' nonlinear mixed-effects models (arXiv:2411.14185). arXiv. | ||
#' \doi{10.48550/arXiv.2411.14185} | ||
#' | ||
#' **The utility of EDF to diagnose hierarchical model behavior** | ||
#' **The utility of EDF to diagnose hierarchical model behaviour:** | ||
#' | ||
#' Thorson, J. T. (2024). Measuring complexity for hierarchical | ||
#' models using effective degrees of freedom. Ecology, | ||
#' 105(7), e4327 \doi{10.1002/ecy.4327} | ||
#' | ||
#' @examples | ||
#' mesh <- make_mesh(dogfish, c("X", "Y"), cutoff = 15) | ||
#' fit <- sdmTMB(catch_weight ~ s(log(depth)), | ||
#' time_varying = ~1, | ||
#' time_varying_type = "ar1", | ||
#' time = "year", | ||
#' spatiotemporal = "off", | ||
#' mesh = mesh, | ||
#' family = tweedie(), | ||
#' data = dogfish, | ||
#' offset = log(dogfish$area_swept) | ||
#' ) | ||
#' cAIC(fit) | ||
#' cAIC(fit, what = "EDF") | ||
#' AIC(fit) | ||
#' @export | ||
CAIC.sdmTMB <- | ||
function( object, | ||
what = c("CAIC","EDF") ){ | ||
cAIC <- function(object, what = c("cAIC", "EDF"), ...) { | ||
UseMethod("cAIC", object) | ||
} | ||
|
||
what = match.arg(what) | ||
require(Matrix) | ||
tmb_data = object$tmb_data | ||
#' @exportS3Method | ||
cAIC.sdmTMB <- function(object, what = c("cAIC", "EDF")) { | ||
what <- match.arg(what) | ||
what <- tolower(what) | ||
tmb_data <- object$tmb_data | ||
|
||
# Make sure profile = NULL | ||
if( is.null(object$control$profile) ){ | ||
obj = object$tmb_obj | ||
}else{ | ||
obj = TMB::MakeADFun( data = tmb_data, | ||
parameters = object$parlist, | ||
map = object$tmb_map, | ||
random = object$tmb_random, | ||
DLL = "sdmTMB", | ||
profile = NULL ) | ||
## Ensure profile = NULL | ||
if (is.null(object$control$profile)) { | ||
obj <- object$tmb_obj | ||
} else { | ||
obj <- TMB::MakeADFun( | ||
data = tmb_data, | ||
parameters = object$parlist, | ||
map = object$tmb_map, | ||
random = object$tmb_random, | ||
DLL = "sdmTMB", | ||
profile = NULL #< | ||
) | ||
} | ||
|
||
# Make obj_new | ||
tmb_data$weights_i[] = 0 | ||
obj_new = TMB::MakeADFun( data = tmb_data, | ||
parameters = object$parlist, | ||
map = object$tmb_map, | ||
random = object$tmb_random, | ||
DLL = "sdmTMB", | ||
profile = NULL ) | ||
## Make obj_new | ||
tmb_data$weights_i[] <- 0 | ||
obj_new <- TMB::MakeADFun( | ||
data = tmb_data, | ||
parameters = object$parlist, | ||
map = object$tmb_map, | ||
random = object$tmb_random, | ||
DLL = "sdmTMB", | ||
profile = NULL | ||
) | ||
|
||
# | ||
par = obj$env$parList() | ||
par <- obj$env$parList() | ||
parDataMode <- obj$env$last.par | ||
indx = obj$env$lrandom() | ||
q = length(indx) | ||
p = length(object$model$par) | ||
indx <- obj$env$lrandom() | ||
q <- length(indx) | ||
p <- length(object$model$par) | ||
|
||
## use - for Hess because model returns negative loglikelihood; | ||
#cov_Psi_inv = -Hess_new[indx,indx]; ## this is the marginal prec mat of REs; | ||
Hess_new = -Matrix(obj_new$env$f(parDataMode,order=1,type="ADGrad"),sparse = TRUE) | ||
Hess_new = Hess_new[indx,indx] | ||
## use '-' for Hess because model returns negative loglikelihood | ||
Hess_new <- -Matrix::Matrix(obj_new$env$f(parDataMode, order = 1, type = "ADGrad"), sparse = TRUE) | ||
Hess_new <- Hess_new[indx, indx] ## marginal precision matrix of REs | ||
|
||
## Joint hessian etc | ||
Hess = -Matrix(obj$env$f(parDataMode,order=1,type="ADGrad"),sparse = TRUE) | ||
Hess = Hess[indx,indx] | ||
negEDF = diag(solve(Hess, Hess_new)) | ||
Hess <- -Matrix::Matrix(obj$env$f(parDataMode, order = 1, type = "ADGrad"), sparse = TRUE) | ||
Hess <- Hess[indx, indx] | ||
negEDF <- Matrix::diag(Matrix::solve(Hess, Hess_new, sparse = FALSE)) | ||
|
||
if(what=="CAIC"){ | ||
jnll = obj$env$f(parDataMode) | ||
cnll = jnll - obj_new$env$f(parDataMode) | ||
cAIC = 2*cnll + 2*(p+q) - 2*sum(negEDF) | ||
if (what == "caic") { | ||
jnll <- obj$env$f(parDataMode) | ||
cnll <- jnll - obj_new$env$f(parDataMode) | ||
cAIC <- 2 * cnll + 2 * (p + q) - 2 * sum(negEDF) | ||
return(cAIC) | ||
} | ||
if(what=="EDF"){ | ||
# Figure out group for each random-effect coefficient | ||
group = factor(names(object$last.par.best[obj$env$random])) | ||
# Calculate total EDF by group | ||
EDF = tapply(negEDF,INDEX=group,FUN=length) - tapply(negEDF,INDEX=group,FUN=sum) | ||
} else if (what == "edf") { | ||
## Figure out group for each random-effect coefficient | ||
group <- factor(names(object$last.par.best[obj$env$random])) | ||
## Calculate total EDF by group | ||
EDF <- tapply(negEDF, INDEX = group, FUN = length) - tapply(negEDF, INDEX = group, FUN = sum) | ||
return(EDF) | ||
} else { | ||
cli_abort("Option not implemented") | ||
} | ||
} |
This file was deleted.
Oops, something went wrong.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,21 @@ | ||
test_that("cAIC and EDF work", { | ||
skip_on_cran() | ||
skip_on_ci() | ||
mesh <- make_mesh(dogfish, c("X", "Y"), cutoff = 15) | ||
suppressMessages( | ||
fit <- sdmTMB(catch_weight ~ s(log(depth)), | ||
time_varying = ~1, | ||
time_varying_type = "ar1", | ||
time = "year", | ||
spatiotemporal = "off", | ||
mesh = mesh, | ||
family = tweedie(), | ||
data = dogfish, | ||
offset = log(dogfish$area_swept) | ||
) | ||
) | ||
expect_equal(AIC(fit), 12192.9613, tolerance = 1e-1) | ||
expect_equal(cAIC(fit), 12089.4289, tolerance = 1e-1) | ||
edf <- cAIC(fit, what = "EDF") | ||
expect_equal(sum(edf), 54.3870, tolerance = 1e-2) | ||
}) |