R/fit_model.R
In James-Thorson/VAST: Vector-Autoregressive Spatio-Temporal (VAST) Model
Defines functions
predict.fit_model
summary.fit_model
plot.fit_model
print.fit_model
fit_model
Documented in
fit_model
plot.fit_model
predict.fit_model
print.fit_model
summary.fit_model
#' Fit VAST to data
#'
#' This function is the user-interface for the multiple mid-level functions that
#' perform separate components of a spatio-temporal analysis:
#' \itemize{
#' \item determine the extrapolation-grid \code{\link{make_extrapolation_info}},
#' \item define spatial objects \code{\link{make_spatial_info}},
#' \item build covariates from a formula interface \code{\link{make_covariates}},
#' \item assemble data \code{\link[VAST]{make_data}},
#' \item build model \code{\link[VAST]{make_model}},
#' \item estimate parameters \code{fit_tmb}, and
#' \item check for obvious problems with the estimates \code{\link[VAST]{check_fit}}.
#' }
#' Please see reference documetation for each of those functions (e.g., \code{?make_extrapolation_info}) to see a list of arguments used by each mid-level function.
#'
#' Specifically, the mid-level functions called by \code{fit_model(.)} look for arguments in the following order of precedence (from highest to lowest precedence):
#' \enumerate{
#' \item \code{fit_model(.)} prioritizes using named arguments passed directly to \code{fit_model(.)}. If arguments are passed this way, they are used instead of other options below.
#' \item If an argument is not passed supplied directly to \code{fit_model(.)}, then \code{fit_model(.)} looks for elements in input \code{settings}, as typically created by \code{\link{make_settings}}.
#' \item If an argument is not supplied via (1) or (2) above, then each mid-level function uses default values defined in those function arguments, e.g., see \code{args(make_extrapolation_info)} for defaults for function \code{make_extrapolation_info(.)}
#' }
#' Collectively, this order of precedence allows users to specify inputs for a specific project via input method (1), the package author to change defaults through changes in the settings
#' defined for a given purpose in \code{make_settings(.)} via input method (2), while still defaulting to package defaults via option (3).
#'
#' Variables are indexed internally for locations \code{g}, categories \code{c}, and times \code{y}.
#' Location index \code{g} represents Longitude-Latitude \code{fit$extrapolation_list$Data_Extrap[which(fit$spatial_list$g_e==g),c('Lon','Lat')]};
#' Time index \code{y} represents time \code{fit$year_labels}; and
#' Category \code{g} corresponds to values in \code{fit$data_list$g_i}.
#'
#' @inheritParams make_extrapolation_info
#' @inheritParams make_spatial_info
#' @inheritParams make_covariates
#' @inheritParams make_data
#' @inheritParams make_model
#' @inheritParams fit_tmb
#' @param settings Output from \code{\link{make_settings}}
#' @param run_model Boolean indicating whether to run the model or simply return the inputs and built TMB object
#' @param test_fit Boolean indicating whether to apply \code{\link[VAST]{check_fit}} before calculating standard errors, to test for parameters hitting bounds etc; defaults to TRUE
#' @param category_names character vector specifying names for labeling categories \code{c_i}
#' @param year_labels character vector specifying names for labeling times \code{t_i}
#' @param ... additional arguments to pass to \code{\link{make_extrapolation_info}}, \code{\link{make_spatial_info}}, \code{\link[VAST]{make_data}}, \code{\link[VAST]{make_model}}, or \code{fit_tmb},
#' where arguments are matched by name against each function. If an argument doesn't match, it is still passed to \code{\link[VAST]{make_data}}. Note that \code{\link{make_spatial_info}}
#' passes named arguments to \code{\link[fmesher]{fm_mesh_2d}}.
#'
#' @return Object of class \code{fit_model}, containing formatted inputs and outputs from VAST
#' \describe{
#' \item{\code{parameter_estimates}}{Output from \code{fit_tmb}; see that documentation for definition of contents}
#' \item{\code{extrapolation_list}}{Output from \code{\link{make_extrapolation_info}}; see that documentation for definition of contents}
#' \item{\code{spatial_list}}{Output from \code{\link{make_spatial_info}}; see that documentation for definition of contents}
#' \item{\code{data_list}}{Output from \code{\link[VAST]{make_data}}; see that documentation for definition of contents}
#' \item{\code{tmb_list}}{Output from \code{\link[VAST]{make_model}}; see that documentation for definition of contents}
#' \item{\code{ParHat}}{Tagged list of maximum likelihood estimatesion of fixed effects and empirical Bayes estimates of random effects, following format of initial values generated by \code{\link[VAST]{make_parameters}}; see that documentation for definition of contents}
#' \item{\code{Report}}{Tagged list of VAST outputs. For example, estimated density for grid \code{g}, category \code{c}, and time \code{y} is available as \code{fit$Report$D_gcy[g,c,y]}; see Details section for description of indexing}
#' }
#'
#' @family wrapper functions
#' @seealso \code{\link[VAST]{VAST}} for general documentation, \code{\link{make_settings}} for generic settings, \code{\link{fit_model}} for model fitting, and \code{\link{plot_results}} for generic plots
#' @seealso VAST wiki \url{https://github.com/James-Thorson-NOAA/VAST/wiki} for examples documenting many different use-cases and features.
#' @seealso GitHub mainpage \url{https://github.com/James-Thorson-NOAA/VAST#description} for a list of user resources and publications documenting features
#' @seealso \code{\link{summary.fit_model}} for methods to summarize output, including obtain a dataframe of estimated densities and an explanation of DHARMa Probability-Integral-Transform residuals
#' @seealso \code{\link{predict.fit_model}} for methods to predict at new locations using existing or updated covariate values
#'
#' @examples
#' \dontrun{
#' # Load packages
#' library(VAST)
#'
#' # load data set
#' # see `?load_example` for list of stocks with example data
#' # that are installed automatically with `VAST`.
#' example = load_example( data_set="EBS_pollock" )
#'
#' # Make settings
#' settings = make_settings( n_x=50,
#' Region=example$Region,
#' purpose="index",
#' strata.limits=example$strata.limits )
#'
#' # Run model
#' fit = fit_model( "settings"=settings,
#' "Lat_i"=example$sampling_data[,'Lat'],
#' "Lon_i"=example$sampling_data[,'Lon'],
#' "t_i"=example$sampling_data[,'Year'],
#' "c_i"=rep(0,nrow(example$sampling_data)),
#' "b_i"=example$sampling_data[,'Catch_KG'],
#' "a_i"=example$sampling_data[,'AreaSwept_km2'],
#' "v_i"=example$sampling_data[,'Vessel'] )
#'
#' # Plot results
#' plot_results( settings=settings, fit=fit )
#' }
#'
#' @export
#' @md
# Using https://cran.r-project.org/web/packages/roxygen2/vignettes/rd-formatting.html for guidance on markdown-enabled documentation
fit_model <-
function( settings,
Lat_i,
Lon_i,
t_i,
b_i,
a_i,
c_iz = rep(0,length(b_i)),
v_i = rep(0,length(b_i)),
working_dir = tempdir(),
X1config_cp = NULL,
X2config_cp = NULL,
covariate_data,
X1_formula = ~ 0,
X2_formula = ~ 0,
Q1config_k = NULL,
Q2config_k = NULL,
catchability_data,
Q1_formula = ~ 0,
Q2_formula = ~ 0,
newtonsteps = 1,
silent = TRUE,
build_model = TRUE,
run_model = TRUE,
test_fit = TRUE,
category_names = NULL,
year_labels = NULL,
framework = "TMBad",
use_new_epsilon = TRUE,
... ){
# Capture extra arguments to function
extra_args = list(...)
# Backwards-compatible way to capture previous format to input extra arguments for each function via specific input-lists
extra_args = c( extra_args,
extra_args$extrapolation_args,
extra_args$spatial_args,
extra_args$optimize_args,
extra_args$model_args )
start_time = Sys.time()
# Assemble inputs
data_frame = data.frame( "Lat_i"=Lat_i, "Lon_i"=Lon_i, "a_i"=a_i, "v_i"=v_i, "b_i"=b_i, "t_i"=t_i, "c_iz"=c_iz )
# Decide which years to plot
if(is.null(year_labels)) year_labels = paste0( seq(min(t_i),max(t_i)) )
if(is.null(category_names)) category_names = paste0( 1:(max(c_iz,na.rm=TRUE)+1) )
years_to_plot = which( seq(min(t_i),max(t_i)) %in% t_i )
# Save record
message("\n### Writing output from `fit_model` in directory: ", working_dir)
dir.create(working_dir, showWarnings=FALSE, recursive=TRUE)
#save( settings, file=file.path(working_dir,"Record.RData"))
capture.output( settings, file=file.path(working_dir,"settings.txt"))
# Build extrapolation grid
if( is.null(extra_args$extrapolation_list) ){
message("\n### Making extrapolation-grid")
extrapolation_args_default = list(Region = settings$Region,
strata.limits = settings$strata.limits,
zone = settings$zone,
max_cells = settings$max_cells,
DirPath = working_dir)
extrapolation_args_input = combine_lists( input = extra_args,
default = extrapolation_args_default,
args_to_use = formalArgs(make_extrapolation_info) )
extrapolation_list = do.call( what=make_extrapolation_info, args=extrapolation_args_input )
}else{
extrapolation_args_input = NULL
extrapolation_list = extra_args$extrapolation_list
}
# Build information regarding spatial location and correlation
if( is.null(extra_args$spatial_list) ){
message("\n### Making spatial information")
spatial_args_default = list( grid_size_km = settings$grid_size_km,
n_x = settings$n_x,
Method = settings$Method,
Lon_i = Lon_i,
Lat_i = Lat_i,
Extrapolation_List = extrapolation_list,
DirPath = working_dir,
Save_Results = TRUE,
fine_scale = settings$fine_scale,
knot_method = settings$knot_method,
mesh_package = settings$mesh_package )
spatial_args_input = combine_lists( input=extra_args, default=spatial_args_default, args_to_use=c(formalArgs(make_spatial_info),formalArgs(fmesher::fm_mesh_2d)) )
spatial_list = do.call( what=make_spatial_info, args=spatial_args_input )
}else{
spatial_args_input = NULL
spatial_list = extra_args$spatial_list
}
# Build data
# Do *not* restrict inputs to formalArgs(make_data) because other potential inputs are still parsed by make_data for backwards compatibility
if( is.null(extra_args$data_list) ){
message("\n### Making data object") # VAST::
if(missing(covariate_data)) covariate_data = NULL
if(missing(catchability_data)) catchability_data = NULL
data_args_default = list( "Version" = settings$Version,
"FieldConfig" = settings$FieldConfig,
"OverdispersionConfig" = settings$OverdispersionConfig,
"RhoConfig" = settings$RhoConfig,
"VamConfig" = settings$VamConfig,
"ObsModel" = settings$ObsModel,
"c_iz" = c_iz,
"b_i" = b_i,
"a_i" = a_i,
"v_i" = v_i,
"s_i" = spatial_list$knot_i-1,
"t_i" = t_i,
"spatial_list" = spatial_list,
"Options" = settings$Options,
"Aniso" = settings$use_anisotropy,
"X1config_cp" = X1config_cp,
"X2config_cp" = X2config_cp,
"covariate_data" = covariate_data,
"X1_formula" = X1_formula,
"X2_formula" = X2_formula,
"Q1config_k" = Q1config_k,
"Q2config_k" = Q2config_k,
"catchability_data" = catchability_data,
"Q1_formula" = Q1_formula,
"Q2_formula" = Q2_formula )
data_args_input = combine_lists( input=extra_args, default=data_args_default ) # Do *not* use args_to_use
data_list = do.call( what=make_data, args=data_args_input )
#return(data_list) }
}else{
data_args_input = NULL
data_list = extra_args$data_list
}
# Build object
message("\n### Making TMB object")
model_args_default = list("TmbData" = data_list,
"RunDir" = working_dir,
"Version" = settings$Version,
"RhoConfig" = settings$RhoConfig,
"loc_x" = spatial_list$loc_x,
"Method" = spatial_list$Method,
"build_model" = build_model,
"framework" = framework )
model_args_input = combine_lists( input=extra_args, default=model_args_default, args_to_use=formalArgs(make_model) )
tmb_list = do.call( what=make_model, args=model_args_input )
# Run the model or optionally don't
if( run_model==FALSE | build_model==FALSE ){
# Build and output
input_args = list( "extra_args" = extra_args,
"extrapolation_args_input" = extrapolation_args_input,
"model_args_input" = model_args_input,
"spatial_args_input" = spatial_args_input,
"data_args_input" = data_args_input )
Return = list( "data_frame" = data_frame,
"extrapolation_list" = extrapolation_list,
"spatial_list" = spatial_list,
"data_list" = data_list,
"tmb_list" = tmb_list,
"year_labels" = year_labels,
"years_to_plot" = years_to_plot,
"category_names" = category_names,
"settings" = settings,
"input_args" = input_args)
class(Return) = "fit_model"
return(Return)
}
if(silent==TRUE) tmb_list$Obj$env$beSilent()
# Check for obvious problems with model
if( test_fit==TRUE ){
message("\n### Checking model at initial values")
LogLike0 = tmb_list$Obj$fn( tmb_list$Obj$par )
Gradient0 = tmb_list$Obj$gr( tmb_list$Obj$par )
if( any( Gradient0==0 ) ){
message("\n")
stop("Please check model structure; some parameter has a gradient of zero at starting values\n", call.=FALSE)
}else{
message("All fixed effects have a nonzero gradient")
}
}
# Optimize object
message("\n### Estimating parameters")
# have user override upper, lower, and loopnum
optimize_args_default1 = list( lower = tmb_list$Lower,
upper = tmb_list$Upper,
loopnum = 1)
optimize_args_default1 = combine_lists( default=optimize_args_default1, input=extra_args, args_to_use=formalArgs(fit_tmb) )
# auto-override user inputs for optimizer-related inputs for first test run
optimize_args_input1 = list(obj = tmb_list$Obj,
savedir = NULL,
newtonsteps = 0,
bias.correct = FALSE,
control = list(eval.max = 50000, iter.max = 50000, trace = 1),
quiet = TRUE,
getsd = FALSE )
# combine
optimize_args_input1 = combine_lists( default=optimize_args_default1, input=optimize_args_input1, args_to_use=formalArgs(fit_tmb) )
parameter_estimates1 = do.call( what=fit_tmb, args=optimize_args_input1 )
# Check fit of model (i.e., evidence of non-convergence based on bounds, approaching zero, etc)
if(exists("check_fit") & test_fit==TRUE ){
problem_found = check_fit( parameter_estimates1 )
if( problem_found==TRUE ){
message("\n")
stop("Please change model structure to avoid problems with parameter estimates and then re-try; see details in `?check_fit`\n", call.=FALSE)
}
}
# Override default bias-correction
if( (use_new_epsilon==TRUE) & (settings$bias.correct==TRUE) & (framework=="TMBad") & ("Index_ctl" %in% settings$vars_to_correct) ){
settings$vars_to_correct = setdiff(settings$vars_to_correct, c("Index_ctl","Index_cyl"))
# If length(settings$vars_to_correct)==0, then fit_tmb currently bias-corrects all parameters, so fixing that here
if( length(settings$vars_to_correct)==0 ){
settings$bias.correct = FALSE
}
settings$vars_to_correct = c( settings$vars_to_correct, "eps_Index_ctl" )
}
# Restart estimates after checking parameters
optimize_args_default2 = list( obj = tmb_list$Obj,
lower = tmb_list$Lower,
upper = tmb_list$Upper,
savedir = working_dir,
bias.correct = settings$bias.correct,
newtonsteps = newtonsteps,
bias.correct.control = list(sd = FALSE, split = NULL, nsplit = 1, vars_to_correct = settings$vars_to_correct),
control = list(eval.max = 10000, iter.max = 10000, trace = 1),
loopnum = 1,
getJointPrecision = TRUE,
start_time_elapsed = parameter_estimates1$time_for_run )
# combine while over-riding defaults using user inputs
optimize_args_input2 = combine_lists( input=extra_args, default=optimize_args_default2, args_to_use=formalArgs(fit_tmb) )
# over-ride inputs to start from previous MLE
optimize_args_input2 = combine_lists( input=list(startpar=parameter_estimates1$par), default=optimize_args_input2 )
parameter_estimates2 = do.call( what=fit_tmb, args=optimize_args_input2 )
# Override default bias-correction
if( (use_new_epsilon==TRUE) & (framework=="TMBad") & ("eps_Index_ctl" %in% settings$vars_to_correct) & !is.null(parameter_estimates2$SD) ){
message("\n### Applying faster epsilon bias-correction estimator")
fit = list( "parameter_estimates"=parameter_estimates2, "tmb_list"=tmb_list, "input_args"=list("model_args_input"=model_args_input) )
parameter_estimates2$SD = apply_epsilon( fit )
}
# Extract standard outputs
if( "par" %in% names(parameter_estimates2) ){
if( !is.null(tmb_list$Obj$env$intern) && tmb_list$Obj$env$intern==TRUE ){
Report = as.list(tmb_list$Obj$env$reportenv)
}else{
Report = tmb_list$Obj$report()
}
ParHat = tmb_list$Obj$env$parList( parameter_estimates2$par )
# Label stuff
Report = amend_output( Report = Report,
TmbData = data_list,
Map = tmb_list$Map,
Sdreport = parameter_estimates2$SD,
year_labels = year_labels,
category_names = category_names,
extrapolation_list = extrapolation_list )
}else{
Report = ParHat = "Model is not converged"
}
# Build and output
input_args = list( "extra_args" = extra_args,
"extrapolation_args_input" = extrapolation_args_input,
"model_args_input" = model_args_input,
"spatial_args_input" = spatial_args_input,
"optimize_args_input1" = optimize_args_input1,
"optimize_args_input2" = optimize_args_input2,
"data_args_input" = data_args_input )
Return = list( "data_frame" = data_frame,
"extrapolation_list" = extrapolation_list,
"spatial_list" = spatial_list,
"data_list" = data_list,
"tmb_list" = tmb_list,
"parameter_estimates" = parameter_estimates2,
"Report" = Report,
"ParHat" = ParHat,
"year_labels" = year_labels,
"years_to_plot" = years_to_plot,
"category_names" = category_names,
"settings" = settings,
"input_args" = input_args,
"X1config_cp" = X1config_cp,
"X2config_cp" = X2config_cp,
"covariate_data" = covariate_data,
"X1_formula" = X1_formula,
"X2_formula" = X2_formula,
"Q1config_k" = Q1config_k,
"Q2config_k" = Q1config_k,
"catchability_data" = catchability_data,
"Q1_formula" = Q1_formula,
"Q2_formula" = Q2_formula,
"total_time" = Sys.time() - start_time )
# Add stuff for effects package
Return$effects = list()
if( !is.null(catchability_data) ){
catchability_data_full = data.frame( catchability_data, "linear_predictor"=0 )
Q1_formula_full = update.formula(Q1_formula, linear_predictor~.+0)
call_Q1 = lm( Q1_formula_full, data=catchability_data_full)$call
Q2_formula_full = update.formula(Q2_formula, linear_predictor~.+0)
call_Q2 = lm( Q2_formula_full, data=catchability_data_full)$call
Return$effects = c( Return$effects, list(call_Q1=call_Q1, call_Q2=call_Q2, catchability_data_full=catchability_data_full) )
}
if( !is.null(covariate_data) ){
covariate_data_full = data.frame( covariate_data, "linear_predictor"=0 )
X1_formula_full = update.formula(X1_formula, linear_predictor~.+0)
call_X1 = lm( X1_formula_full, data=covariate_data_full)$call
X2_formula_full = update.formula(X2_formula, linear_predictor~.+0)
call_X2 = lm( X2_formula_full, data=covariate_data_full)$call
Return$effects = c( Return$effects, list(call_X1=call_X1, call_X2=call_X2, covariate_data_full=covariate_data_full) )
}
# Add stuff for marginaleffects package
Return$last.par.best = tmb_list$Obj$env$last.par.best
# class and return
class(Return) = "fit_model"
return( Return )
}
#' Print parameter estimates and standard errors.
#'
#' @title Print parameter estimates
#' @param x Output from \code{\link{fit_model}}
#' @param ... Not used
#' @return NULL
#' @method print fit_model
#' @export
print.fit_model <- function(x, ...)
{
cat("fit_model(.) result\n")
if( "parameter_estimates" %in% names(x) ){
print( x$parameter_estimates )
}else{
cat("`parameter_estimates` not available in `fit_model`\n")
}
invisible(x$parameter_estimates)
}
#' Print parameter estimates and standard errors.
#'
#' @title Print parameter estimates
#' @param fit Output from \code{\link{fit_model}}
#' @param what String specifying what elements of results to plot; options include `extrapolation_grid`, `spatial_mesh`, and `results`
#' @param ... Arguments passed to \code{\link{plot_results}}
#' @return NULL
#' @method plot fit_model
#' @export
plot.fit_model <- function(x, what="results", ...)
{
if(!is.character(what)) stop("Check `what` in `plot.fit_model`")
## Plot extrapolation-grid
if( length(grep(what, "extrapolation_grid")) ){
cat("\n### Running `plot.make_extrapolation_info`\n")
plot( x$extrapolation_list )
return(invisible(NULL))
}
## Plot extrapolation-grid
if( length(grep(what, c("spatial_info","inla_mesh"))) ){
cat("\n### Running `plot.make_spatial_info`\n")
plot( x$spatial_list )
return(invisible(NULL))
}
# diagnostic plots
if( length(grep(what, "results")) ){
cat("\n### Running `plot_results`\n")
ans = plot_results( x, ... )
return(invisible(ans))
}
stop( "input `what` not matching available options" )
}
#' Extract summary of spatial estimates
#'
#' \code{summary.fit_model} extracts commonly used quantities derived from a fitted VAST model
#'
#' \code{summary.fit_model} faciliates common queries for model output including:
#' \itemize{
#' \item \code{what="density"} returns a tagged list containing element \code{Density_dataframe},
#' which lists the estimated density for every Latitude-Longitude-Year-Category combination
#' for every modelled location in the extrapolation-grid.
#' \item \code{what="residuals"} returns a DHARMa object containing PIT residuals;
#' See details section for more information.
#' }
#'
#' For calculating residuals, the function calls package \code{\link[DHARMa]{DHARMa}}
#' to create a diagnostic object for simulation-based quantile residuals.
#' It specifically simulates replicated data sets from the predictive distribution of data
#' conditional on estimated fixed and random effects. It then
#' calculates probability-integral-transform (PIT) residuals from the observed and simulated values.
#' It then replaces the automatically calculated residuals in the DHARMa object with these these PIT residuals,
#' so that DHARMa can be used to plot those PIT residuals. PIT residuals are used because the original DHARMa calculations
#' are not correct when using a delta-model (due to additional jittered values added by DHARMa when detecting multiple 0-valued observations), hence
#' the need to call this function to correctly calculate PIT residuals for a delta-model.
#'
#' Note that \code{summary(fit, ..., type=0} uses \code{\link{oneStepPredict_deltaModel}} to calculate one-step-ahead
#' residuals. These are probably the most appropriate method for evaluating residuals, but are also *very* slow to calculate relative
#' to other methods.
#'
#' @inheritParams simulate_data
#' @inheritParams DHARMa::plotResiduals
#'
#' @param x Output from \code{\link{fit_model}}
#' @param what String indicating what to summarize; options are `density`, `index` or `residuals`
#' @param n_samples Number of samples used when \code{what="residuals"}
#' @param ... additional arguments passed to \code{\link[DHARMa]{plotResiduals}} when \code{what="residuals"}
#'
#' @return NULL
#' @seealso \code{\link{plot_quantile_residuals}} to plot output of \code{summary.fit_model(x,what="residuals")}
#' @method summary fit_model
#' @export
summary.fit_model <-
function( x,
what = "density",
n_samples = 250,
working_dir = NULL,
type = 1,
random_seed = NULL,
form = NULL,
category_names = x$category_names,
year_labels = x$year_labels,
...)
{
ans = NULL
# Check and implement units and labels
x$Report = amend_output( fit = x,
year_labels = year_labels,
category_names = category_names )
if( tolower(what) %in% c("density","index") ){
# Load location of extrapolation-grid
ans[["extrapolation_grid"]] = print( x$extrapolation_list, quiet=TRUE )
# Load density estimates
if( tolower(what) == "density" ){
ans[["Density_array"]] = x$Report[["D_gct"]]
}else if( tolower(what) == "index" ){
ans[["Density_array"]] = x$Report[["Index_gctl"]]
}
# Exclude boundary knots
if( !( x$settings$fine_scale==TRUE | x$spatial_list$Method=="Stream_network" ) ){
index_tmp = x$spatial_list$NN_Extrap$nn.idx[ which(x$extrapolation_list[["Area_km2_x"]]>0), 1 ]
ans[["Density_array"]] = ans[["Density_array"]][ index_tmp,,,drop=FALSE]
}
# Error check
if( any(sapply(dimnames(ans[["Density_array"]]),FUN=is.null)) ){
stop("`summary.fit_model` assumes that arrays are labeled")
}
# Expand as grid
Density_dataframe = expand.grid( dimnames(ans[["Density_array"]]) )
Density_dataframe = cbind( Density_dataframe, ans[["extrapolation_grid"]][Density_dataframe[,'Site'],], as.vector(ans[["Density_array"]]) )
colnames(Density_dataframe)[ncol(Density_dataframe)] = tolower(what)
# Save output
ans[["Density_dataframe"]] = Density_dataframe
ans[['year_labels']] = x[['year_labels']]
rownames(Density_dataframe) = NULL
# Print to terminal
cat("\n### Printing head of and tail `Density_dataframe`, and returning data frame in output object\n")
print(head(Density_dataframe))
print(tail(Density_dataframe))
}
# Residuals
if( tolower(what) == "residuals" ){
# extract objects
Obj = x$tmb_list$Obj
# Change n_g
# Must change back explicitly because TMB appears to pass env as a pointer, so changes in copy affect original x outside of function!
n_g_orig = Obj$env$data$n_g
revert_settings = function(n_g){Obj$env$data$n_g = n_g}
on.exit( revert_settings(n_g_orig) )
Obj$env$data$n_g = 0
if( type %in% c(1,4) ){
b_iz = matrix(NA, nrow=length(x$data_list$b_i), ncol=n_samples)
message( "Sampling from the distribution of data conditional on estimated fixed and random effects" )
for( zI in 1:n_samples ){
if( zI%%max(1,floor(n_samples/10)) == 0 ){
message( " Finished sample ", zI, " of ",n_samples )
}
b_iz[,zI] = simulate_data( fit=list(tmb_list=list(Obj=Obj)), type=type, random_seed=list(random_seed+zI,NULL)[[1+is.null(random_seed)]] )$b_i
}
#if( any(is.na(x$data_list$b_i)) ){
# stop("dharmaRes not designed to work when any observations have b_i=NA")
#}
# Substitute any observation where b_i = NA with all zeros, which will then have a uniform PIT
which_na = which(is.na(x$data_list$b_i))
if( length(which_na) > 0 ){
x$data_list$b_i[which_na] = 0
b_iz[which_na,] = 0
warning("When calculating DHARMa residuals, replacing instances where b_i=NA with a uniform PIT residual")
}
if( any(is.na(b_iz)) ){
stop("Check simulated residuals for NA values")
}
# Run DHARMa
# Adding jitters because DHARMa version 0.3.2.0 sometimes still throws an error method="traditional" and integer=FALSE without jitters
dharmaRes = DHARMa::createDHARMa( simulatedResponse = strip_units(b_iz) + 1e-10*array(rnorm(prod(dim(b_iz))),dim=dim(b_iz)),
observedResponse = strip_units(x$data_list$b_i) + 1e-10*rnorm(length(x$data_list$b_i)),
fittedPredictedResponse = strip_units(x$Report$D_i),
integer = FALSE)
#dharmaRes = DHARMa::createDHARMa(simulatedResponse=strip_units(b_iz),
# observedResponse=strip_units(x$data_list$b_i),
# fittedPredictedResponse=strip_units(x$Report$D_i),
# method="PIT")
# Save to report error
if( FALSE ){
all = list( simulatedResponse=strip_units(b_iz), observedResponse=strip_units(x$data_list$b_i), fittedPredictedResponse=strip_units(x$Report$D_i) )
#save(all, file=paste0(root_dir,"all.RData") )
dharmaRes = DHARMa::createDHARMa(simulatedResponse=all$simulatedResponse + rep(1,nrow(all$simulatedResponse))%o%c(0.001*rnorm(1),rep(0,ncol(all$simulatedResponse)-1)),
observedResponse=all$observedResponse,
fittedPredictedResponse=all$fittedPredictedResponse,
method="PIT")
}
# Calculate probability-integral-transform (PIT) residuals
message( "Substituting probability-integral-transform (PIT) residuals for DHARMa-calculated residuals" )
prop_lessthan_i = apply( b_iz<outer(x$data_list$b_i,rep(1,n_samples)),
MARGIN=1,
FUN=mean )
prop_lessthanorequalto_i = apply( b_iz<=outer(x$data_list$b_i,rep(1,n_samples)),
MARGIN=1,
FUN=mean )
PIT_i = runif(min=prop_lessthan_i, max=prop_lessthanorequalto_i, n=length(prop_lessthan_i) )
# cbind( "Difference"=dharmaRes$scaledResiduals - PIT_i, "PIT"=PIT_i, "Original"=dharmaRes$scaledResiduals, "b_i"=x$data_list$b_i )
dharmaRes$scaledResiduals = PIT_i
}else if( type==0 ){
# Check for issues
if( !all(x$data_list$ObsModel_ez[,1] %in% c(1,2)) ){
stop("oneStepAhead residuals only code for gamma and lognormal distributions")
}
# Run OSA
message( "Running oneStepPredict_deltaModel for each observation, to then load them into DHARMa object for plotting" )
osa = oneStepPredict_deltaModel( obj = x$tmb_list$Obj,
observation.name = "b_i",
method = "cdf",
data.term.indicator = "keep",
deltaSupport = 0,
trace = TRUE )
# Build DHARMa object on fake inputs and load OSA into DHARMa object
dharmaRes = DHARMa::createDHARMa(simulatedResponse=matrix(rnorm(x$data_list$n_i*10,mean=x$data_list$b_i),ncol=10),
observedResponse=x$data_list$b_i,
fittedPredictedResponse=x$Report$D_i,
integer=FALSE)
dharmaRes$scaledResiduals = pnorm(osa$residual)
}else{
stop("`type` only makes sense for 0 (oneStepAhead), 1 (conditional, a.k.a. measurement error) or 4 (unconditional) simulations")
}
# do plot
if( is.null(working_dir) ){
plot_dharma(dharmaRes, ...)
}else if(!is.na(working_dir) ){
png(file=file.path(working_dir,"quantile_residuals.png"), width=8, height=4, res=200, units='in')
plot_dharma(dharmaRes, form=form, ...)
dev.off()
}
# Return stuff
ans = dharmaRes
message( "Invisibly returning output from `DHARMa::createDHARMa`, e.g., to apply `plot.DHARMa` to this output")
}
if( tolower(what) %in% c("parhat","estimates") ){
ans[["estimates"]] = x$ParHat
cat("\n### Printing slots of `ParHat`, and returning list in output object")
print(names(x$ParHat))
}
if( is.null(ans) ){
stop( "`summary.fit_model` not implemented for inputted value of argument `what`" )
}
# diagnostic plots
return(invisible(ans))
}
#' Predict density for new samples (\emph{Beta version; may change without notice})
#'
#' \code{predict.fit_model} calculates predictions given new data
#'
#' \code{predict.fit_model} is designed with two purposes in mind:
#' \enumerate{
#' \item If \code{new_covariate_data=NULL} as by default, then the model uses the covariate values supplied during original model fits,
#' and interpolates as needed from those supplied values to new predicted locations. This then uses *exactly* the same information
#' as was available during model fitting.
#' \item If \code{new_covariate_data} is supplied with new values (e.g., at locations for predictions), then these values are used in
#' combination with original covariate values when interpolating to new values. However, supplying \code{new_oovariate_data}
#' at the same Lat-Lon-Year combination as any original covariate value will delete those matches in the latter, such that originally fitted data
#' can be predicted using alternative values for covariates (e.g., when calculating partial dependence plots)
#' }
#'
#' @inheritParams make_covariates
#' @inheritParams make_data
#' @param x Output from \code{\link{fit_model}}
#' @param what Which output from \code{fit$Report} should be extracted; default is predicted density
#' @param keep_old_covariates Whether to add new_covariate_data to existing data.
#' This is useful when predicting values at new locations, but does not work
#' when predicting data are locations with existing data (because the interpolation of
#' covariate values will conflict for existing and new covariate values), e.g.,
#' when calculating partial dependence plots for existing data.
#'
#' @return NULL
#'
#' @examples
#' \dontrun{
#'
#' # Showing use of package pdp for partial dependence plots
#' pred.fun = function( object, newdata ){
#' predict( x=object,
#' Lat_i = object$data_frame$Lat_i,
#' Lon_i = object$data_frame$Lon_i,
#' t_i = object$data_frame$t_i,
#' a_i = object$data_frame$a_i,
#' what = "P1_iz",
#' new_covariate_data = newdata,
#' do_checks = FALSE )
#' }
#'
#' library(ggplot2)
#' library(pdp)
#' Partial = partial( object = fit,
#' pred.var = "BOT_DEPTH",
#' pred.fun = pred.fun,
#' train = fit$covariate_data )
#' autoplot(Partial)
#'
#' }
#'
#' @method predict fit_model
#' @export
predict.fit_model <- function(x,
what="D_i",
Lat_i,
Lon_i,
t_i,
a_i,
c_iz = rep(0,length(t_i)),
v_i = rep(0,length(t_i)),
new_covariate_data = NULL,
new_catchability_data = NULL,
do_checks = TRUE,
working_dir = getwd() )
{
message("`predict.fit_model(.)` is in beta-testing, and please explore results carefully prior to using")
# Check issues
if( !(what%in%names(x$Report)) || (length(x$Report[[what]])!=x$data_list$n_i) ){
stop("`what` can only take a few options")
}
if( !is.null(new_covariate_data) ){
# Confirm all columns are available
if( !all(colnames(x$covariate_data) %in% colnames(new_covariate_data)) ){
stop("Please ensure that all columns of `x$covariate_data` are present in `new_covariate_data`")
}
# Eliminate unnecessary columns
new_covariate_data = new_covariate_data[,match(colnames(x$covariate_data),colnames(new_covariate_data))]
# Eliminate old-covariates that are also present in new_covariate_data
NN = RANN::nn2( query=x$covariate_data[,c('Lat','Lon','Year')], data=new_covariate_data[,c('Lat','Lon','Year')], k=1 )
if( any(NN$nn.dist==0) ){
x$covariate_data = x$covariate_data[-which(NN$nn.dist==0),,drop=FALSE]
}
}
if( !is.null(new_catchability_data) ){
stop("Option not implemented")
}
# Process covariates
catchability_data = rbind( x$catchability_data, new_catchability_data )
covariate_data = rbind( x$covariate_data, new_covariate_data )
# Process inputs
PredTF_i = c( x$data_list$PredTF_i, rep(1,length(t_i)) )
b_i = c( x$data_frame[,"b_i"], rep(1,length(t_i)) )
c_iz = rbind( matrix(x$data_frame[,grep("c_iz",names(x$data_frame))]), matrix(c_iz) )
Lat_i = c( x$data_frame[,"Lat_i"], Lat_i )
Lon_i = c( x$data_frame[,"Lon_i"], Lon_i )
a_i = c( x$data_frame[,"a_i"], a_i )
v_i = c( x$data_frame[,"v_i"], v_i )
t_i = c( x$data_frame[,"t_i"], t_i )
#assign("b_i", b_i, envir=.GlobalEnv)
# Build information regarding spatial location and correlation
message("\n### Re-making spatial information")
spatial_args_new = list("anisotropic_mesh"=x$spatial_list$MeshList$anisotropic_mesh, "Kmeans"=x$spatial_list$Kmeans,
"Lon_i"=Lon_i, "Lat_i"=Lat_i )
spatial_args_input = combine_lists( input=spatial_args_new, default=x$input_args$spatial_args_input )
spatial_list = do.call( what=make_spatial_info, args=spatial_args_input )
# Check spatial_list
if( !all.equal(spatial_list$MeshList,x$spatial_list$MeshList) ){
stop("`MeshList` generated during `predict.fit_model` doesn't match that of original fit; please email package author to report issue")
}
# Build data
# Do *not* restrict inputs to formalArgs(make_data) because other potential inputs are still parsed by make_data for backwards compatibility
message("\n### Re-making data object")
data_args_new = list( "c_iz"=c_iz, "b_i"=b_i, "a_i"=a_i, "v_i"=v_i, "PredTF_i"=PredTF_i,
"t_i"=t_i, "spatial_list"=spatial_list,
"covariate_data"=covariate_data, "catchability_data"=catchability_data )
data_args_input = combine_lists( input=data_args_new, default=x$input_args$data_args_input ) # Do *not* use args_to_use
data_list = do.call( what=make_data, args=data_args_input )
data_list$n_g = 0
# Build object
message("\n### Re-making TMB object")
model_args_default = list("TmbData"=data_list, "RunDir"=working_dir, "Version"=x$settings$Version,
"RhoConfig"=x$settings$RhoConfig, "loc_x"=spatial_list$loc_x, "Method"=spatial_list$Method)
model_args_input = combine_lists( input=list("Parameters"=x$ParHat),
default=model_args_default, args_to_use=formalArgs(make_model) )
tmb_list = do.call( what=make_model, args=model_args_input )
# Extract output
Report = tmb_list$Obj$report()
Y_i = Report[[what]][(1+nrow(x$data_frame)):length(Report$D_i)]
# sanity check
#if( all.equal(covariate_data,x$covariate_data) & Report$jnll!=x$Report$jnll){
if( do_checks==TRUE && (Report$jnll!=x$Report$jnll) ){
message("Problem detected in `predict.fit_model`; returning outputs for diagnostic purposes")
Return = list("Report"=Report, "data_list"=data_list)
return(Return)
}
# return prediction
return(Y_i)
}
James-Thorson/VAST documentation built on Feb. 9, 2025, 9:05 a.m.
R Package Documentation
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Defines functions predict.fit_model summary.fit_model plot.fit_model print.fit_model fit_model
Documented in fit_model plot.fit_model predict.fit_model print.fit_model summary.fit_model
#' Fit VAST to data
#'
#' This function is the user-interface for the multiple mid-level functions that
#' perform separate components of a spatio-temporal analysis:
#' \itemize{
#' \item determine the extrapolation-grid \code{\link{make_extrapolation_info}},
#' \item define spatial objects \code{\link{make_spatial_info}},
#' \item build covariates from a formula interface \code{\link{make_covariates}},
#' \item assemble data \code{\link[VAST]{make_data}},
#' \item build model \code{\link[VAST]{make_model}},
#' \item estimate parameters \code{fit_tmb}, and
#' \item check for obvious problems with the estimates \code{\link[VAST]{check_fit}}.
#' }
#' Please see reference documetation for each of those functions (e.g., \code{?make_extrapolation_info}) to see a list of arguments used by each mid-level function.
#'
#' Specifically, the mid-level functions called by \code{fit_model(.)} look for arguments in the following order of precedence (from highest to lowest precedence):
#' \enumerate{
#' \item \code{fit_model(.)} prioritizes using named arguments passed directly to \code{fit_model(.)}. If arguments are passed this way, they are used instead of other options below.
#' \item If an argument is not passed supplied directly to \code{fit_model(.)}, then \code{fit_model(.)} looks for elements in input \code{settings}, as typically created by \code{\link{make_settings}}.
#' \item If an argument is not supplied via (1) or (2) above, then each mid-level function uses default values defined in those function arguments, e.g., see \code{args(make_extrapolation_info)} for defaults for function \code{make_extrapolation_info(.)}
#' }
#' Collectively, this order of precedence allows users to specify inputs for a specific project via input method (1), the package author to change defaults through changes in the settings
#' defined for a given purpose in \code{make_settings(.)} via input method (2), while still defaulting to package defaults via option (3).
#'
#' Variables are indexed internally for locations \code{g}, categories \code{c}, and times \code{y}.
#' Location index \code{g} represents Longitude-Latitude \code{fit$extrapolation_list$Data_Extrap[which(fit$spatial_list$g_e==g),c('Lon','Lat')]};
#' Time index \code{y} represents time \code{fit$year_labels}; and
#' Category \code{g} corresponds to values in \code{fit$data_list$g_i}.
#'
#' @inheritParams make_extrapolation_info
#' @inheritParams make_spatial_info
#' @inheritParams make_covariates
#' @inheritParams make_data
#' @inheritParams make_model
#' @inheritParams fit_tmb
#' @param settings Output from \code{\link{make_settings}}
#' @param run_model Boolean indicating whether to run the model or simply return the inputs and built TMB object
#' @param test_fit Boolean indicating whether to apply \code{\link[VAST]{check_fit}} before calculating standard errors, to test for parameters hitting bounds etc; defaults to TRUE
#' @param category_names character vector specifying names for labeling categories \code{c_i}
#' @param year_labels character vector specifying names for labeling times \code{t_i}
#' @param ... additional arguments to pass to \code{\link{make_extrapolation_info}}, \code{\link{make_spatial_info}}, \code{\link[VAST]{make_data}}, \code{\link[VAST]{make_model}}, or \code{fit_tmb},
#' where arguments are matched by name against each function. If an argument doesn't match, it is still passed to \code{\link[VAST]{make_data}}. Note that \code{\link{make_spatial_info}}
#' passes named arguments to \code{\link[fmesher]{fm_mesh_2d}}.
#'
#' @return Object of class \code{fit_model}, containing formatted inputs and outputs from VAST
#' \describe{
#' \item{\code{parameter_estimates}}{Output from \code{fit_tmb}; see that documentation for definition of contents}
#' \item{\code{extrapolation_list}}{Output from \code{\link{make_extrapolation_info}}; see that documentation for definition of contents}
#' \item{\code{spatial_list}}{Output from \code{\link{make_spatial_info}}; see that documentation for definition of contents}
#' \item{\code{data_list}}{Output from \code{\link[VAST]{make_data}}; see that documentation for definition of contents}
#' \item{\code{tmb_list}}{Output from \code{\link[VAST]{make_model}}; see that documentation for definition of contents}
#' \item{\code{ParHat}}{Tagged list of maximum likelihood estimatesion of fixed effects and empirical Bayes estimates of random effects, following format of initial values generated by \code{\link[VAST]{make_parameters}}; see that documentation for definition of contents}
#' \item{\code{Report}}{Tagged list of VAST outputs. For example, estimated density for grid \code{g}, category \code{c}, and time \code{y} is available as \code{fit$Report$D_gcy[g,c,y]}; see Details section for description of indexing}
#' }
#'
#' @family wrapper functions
#' @seealso \code{\link[VAST]{VAST}} for general documentation, \code{\link{make_settings}} for generic settings, \code{\link{fit_model}} for model fitting, and \code{\link{plot_results}} for generic plots
#' @seealso VAST wiki \url{https://github.com/James-Thorson-NOAA/VAST/wiki} for examples documenting many different use-cases and features.
#' @seealso GitHub mainpage \url{https://github.com/James-Thorson-NOAA/VAST#description} for a list of user resources and publications documenting features
#' @seealso \code{\link{summary.fit_model}} for methods to summarize output, including obtain a dataframe of estimated densities and an explanation of DHARMa Probability-Integral-Transform residuals
#' @seealso \code{\link{predict.fit_model}} for methods to predict at new locations using existing or updated covariate values
#'
#' @examples
#' \dontrun{
#' # Load packages
#' library(VAST)
#'
#' # load data set
#' # see `?load_example` for list of stocks with example data
#' # that are installed automatically with `VAST`.
#' example = load_example( data_set="EBS_pollock" )
#'
#' # Make settings
#' settings = make_settings( n_x=50,
#' Region=example$Region,
#' purpose="index",
#' strata.limits=example$strata.limits )
#'
#' # Run model
#' fit = fit_model( "settings"=settings,
#' "Lat_i"=example$sampling_data[,'Lat'],
#' "Lon_i"=example$sampling_data[,'Lon'],
#' "t_i"=example$sampling_data[,'Year'],
#' "c_i"=rep(0,nrow(example$sampling_data)),
#' "b_i"=example$sampling_data[,'Catch_KG'],
#' "a_i"=example$sampling_data[,'AreaSwept_km2'],
#' "v_i"=example$sampling_data[,'Vessel'] )
#'
#' # Plot results
#' plot_results( settings=settings, fit=fit )
#' }
#'
#' @export
#' @md
# Using https://cran.r-project.org/web/packages/roxygen2/vignettes/rd-formatting.html for guidance on markdown-enabled documentation
fit_model <-
function( settings,
Lat_i,
Lon_i,
t_i,
b_i,
a_i,
c_iz = rep(0,length(b_i)),
v_i = rep(0,length(b_i)),
working_dir = tempdir(),
X1config_cp = NULL,
X2config_cp = NULL,
covariate_data,
X1_formula = ~ 0,
X2_formula = ~ 0,
Q1config_k = NULL,
Q2config_k = NULL,
catchability_data,
Q1_formula = ~ 0,
Q2_formula = ~ 0,
newtonsteps = 1,
silent = TRUE,
build_model = TRUE,
run_model = TRUE,
test_fit = TRUE,
category_names = NULL,
year_labels = NULL,
framework = "TMBad",
use_new_epsilon = TRUE,
... ){
# Capture extra arguments to function
extra_args = list(...)
# Backwards-compatible way to capture previous format to input extra arguments for each function via specific input-lists
extra_args = c( extra_args,
extra_args$extrapolation_args,
extra_args$spatial_args,
extra_args$optimize_args,
extra_args$model_args )
start_time = Sys.time()
# Assemble inputs
data_frame = data.frame( "Lat_i"=Lat_i, "Lon_i"=Lon_i, "a_i"=a_i, "v_i"=v_i, "b_i"=b_i, "t_i"=t_i, "c_iz"=c_iz )
# Decide which years to plot
if(is.null(year_labels)) year_labels = paste0( seq(min(t_i),max(t_i)) )
if(is.null(category_names)) category_names = paste0( 1:(max(c_iz,na.rm=TRUE)+1) )
years_to_plot = which( seq(min(t_i),max(t_i)) %in% t_i )
# Save record
message("\n### Writing output from `fit_model` in directory: ", working_dir)
dir.create(working_dir, showWarnings=FALSE, recursive=TRUE)
#save( settings, file=file.path(working_dir,"Record.RData"))
capture.output( settings, file=file.path(working_dir,"settings.txt"))
# Build extrapolation grid
if( is.null(extra_args$extrapolation_list) ){
message("\n### Making extrapolation-grid")
extrapolation_args_default = list(Region = settings$Region,
strata.limits = settings$strata.limits,
zone = settings$zone,
max_cells = settings$max_cells,
DirPath = working_dir)
extrapolation_args_input = combine_lists( input = extra_args,
default = extrapolation_args_default,
args_to_use = formalArgs(make_extrapolation_info) )
extrapolation_list = do.call( what=make_extrapolation_info, args=extrapolation_args_input )
}else{
extrapolation_args_input = NULL
extrapolation_list = extra_args$extrapolation_list
}
# Build information regarding spatial location and correlation
if( is.null(extra_args$spatial_list) ){
message("\n### Making spatial information")
spatial_args_default = list( grid_size_km = settings$grid_size_km,
n_x = settings$n_x,
Method = settings$Method,
Lon_i = Lon_i,
Lat_i = Lat_i,
Extrapolation_List = extrapolation_list,
DirPath = working_dir,
Save_Results = TRUE,
fine_scale = settings$fine_scale,
knot_method = settings$knot_method,
mesh_package = settings$mesh_package )
spatial_args_input = combine_lists( input=extra_args, default=spatial_args_default, args_to_use=c(formalArgs(make_spatial_info),formalArgs(fmesher::fm_mesh_2d)) )
spatial_list = do.call( what=make_spatial_info, args=spatial_args_input )
}else{
spatial_args_input = NULL
spatial_list = extra_args$spatial_list
}
# Build data
# Do *not* restrict inputs to formalArgs(make_data) because other potential inputs are still parsed by make_data for backwards compatibility
if( is.null(extra_args$data_list) ){
message("\n### Making data object") # VAST::
if(missing(covariate_data)) covariate_data = NULL
if(missing(catchability_data)) catchability_data = NULL
data_args_default = list( "Version" = settings$Version,
"FieldConfig" = settings$FieldConfig,
"OverdispersionConfig" = settings$OverdispersionConfig,
"RhoConfig" = settings$RhoConfig,
"VamConfig" = settings$VamConfig,
"ObsModel" = settings$ObsModel,
"c_iz" = c_iz,
"b_i" = b_i,
"a_i" = a_i,
"v_i" = v_i,
"s_i" = spatial_list$knot_i-1,
"t_i" = t_i,
"spatial_list" = spatial_list,
"Options" = settings$Options,
"Aniso" = settings$use_anisotropy,
"X1config_cp" = X1config_cp,
"X2config_cp" = X2config_cp,
"covariate_data" = covariate_data,
"X1_formula" = X1_formula,
"X2_formula" = X2_formula,
"Q1config_k" = Q1config_k,
"Q2config_k" = Q2config_k,
"catchability_data" = catchability_data,
"Q1_formula" = Q1_formula,
"Q2_formula" = Q2_formula )
data_args_input = combine_lists( input=extra_args, default=data_args_default ) # Do *not* use args_to_use
data_list = do.call( what=make_data, args=data_args_input )
#return(data_list) }
}else{
data_args_input = NULL
data_list = extra_args$data_list
}
# Build object
message("\n### Making TMB object")
model_args_default = list("TmbData" = data_list,
"RunDir" = working_dir,
"Version" = settings$Version,
"RhoConfig" = settings$RhoConfig,
"loc_x" = spatial_list$loc_x,
"Method" = spatial_list$Method,
"build_model" = build_model,
"framework" = framework )
model_args_input = combine_lists( input=extra_args, default=model_args_default, args_to_use=formalArgs(make_model) )
tmb_list = do.call( what=make_model, args=model_args_input )
# Run the model or optionally don't
if( run_model==FALSE | build_model==FALSE ){
# Build and output
input_args = list( "extra_args" = extra_args,
"extrapolation_args_input" = extrapolation_args_input,
"model_args_input" = model_args_input,
"spatial_args_input" = spatial_args_input,
"data_args_input" = data_args_input )
Return = list( "data_frame" = data_frame,
"extrapolation_list" = extrapolation_list,
"spatial_list" = spatial_list,
"data_list" = data_list,
"tmb_list" = tmb_list,
"year_labels" = year_labels,
"years_to_plot" = years_to_plot,
"category_names" = category_names,
"settings" = settings,
"input_args" = input_args)
class(Return) = "fit_model"
return(Return)
}
if(silent==TRUE) tmb_list$Obj$env$beSilent()
# Check for obvious problems with model
if( test_fit==TRUE ){
message("\n### Checking model at initial values")
LogLike0 = tmb_list$Obj$fn( tmb_list$Obj$par )
Gradient0 = tmb_list$Obj$gr( tmb_list$Obj$par )
if( any( Gradient0==0 ) ){
message("\n")
stop("Please check model structure; some parameter has a gradient of zero at starting values\n", call.=FALSE)
}else{
message("All fixed effects have a nonzero gradient")
}
}
# Optimize object
message("\n### Estimating parameters")
# have user override upper, lower, and loopnum
optimize_args_default1 = list( lower = tmb_list$Lower,
upper = tmb_list$Upper,
loopnum = 1)
optimize_args_default1 = combine_lists( default=optimize_args_default1, input=extra_args, args_to_use=formalArgs(fit_tmb) )
# auto-override user inputs for optimizer-related inputs for first test run
optimize_args_input1 = list(obj = tmb_list$Obj,
savedir = NULL,
newtonsteps = 0,
bias.correct = FALSE,
control = list(eval.max = 50000, iter.max = 50000, trace = 1),
quiet = TRUE,
getsd = FALSE )
# combine
optimize_args_input1 = combine_lists( default=optimize_args_default1, input=optimize_args_input1, args_to_use=formalArgs(fit_tmb) )
parameter_estimates1 = do.call( what=fit_tmb, args=optimize_args_input1 )
# Check fit of model (i.e., evidence of non-convergence based on bounds, approaching zero, etc)
if(exists("check_fit") & test_fit==TRUE ){
problem_found = check_fit( parameter_estimates1 )
if( problem_found==TRUE ){
message("\n")
stop("Please change model structure to avoid problems with parameter estimates and then re-try; see details in `?check_fit`\n", call.=FALSE)
}
}
# Override default bias-correction
if( (use_new_epsilon==TRUE) & (settings$bias.correct==TRUE) & (framework=="TMBad") & ("Index_ctl" %in% settings$vars_to_correct) ){
settings$vars_to_correct = setdiff(settings$vars_to_correct, c("Index_ctl","Index_cyl"))
# If length(settings$vars_to_correct)==0, then fit_tmb currently bias-corrects all parameters, so fixing that here
if( length(settings$vars_to_correct)==0 ){
settings$bias.correct = FALSE
}
settings$vars_to_correct = c( settings$vars_to_correct, "eps_Index_ctl" )
}
# Restart estimates after checking parameters
optimize_args_default2 = list( obj = tmb_list$Obj,
lower = tmb_list$Lower,
upper = tmb_list$Upper,
savedir = working_dir,
bias.correct = settings$bias.correct,
newtonsteps = newtonsteps,
bias.correct.control = list(sd = FALSE, split = NULL, nsplit = 1, vars_to_correct = settings$vars_to_correct),
control = list(eval.max = 10000, iter.max = 10000, trace = 1),
loopnum = 1,
getJointPrecision = TRUE,
start_time_elapsed = parameter_estimates1$time_for_run )
# combine while over-riding defaults using user inputs
optimize_args_input2 = combine_lists( input=extra_args, default=optimize_args_default2, args_to_use=formalArgs(fit_tmb) )
# over-ride inputs to start from previous MLE
optimize_args_input2 = combine_lists( input=list(startpar=parameter_estimates1$par), default=optimize_args_input2 )
parameter_estimates2 = do.call( what=fit_tmb, args=optimize_args_input2 )
# Override default bias-correction
if( (use_new_epsilon==TRUE) & (framework=="TMBad") & ("eps_Index_ctl" %in% settings$vars_to_correct) & !is.null(parameter_estimates2$SD) ){
message("\n### Applying faster epsilon bias-correction estimator")
fit = list( "parameter_estimates"=parameter_estimates2, "tmb_list"=tmb_list, "input_args"=list("model_args_input"=model_args_input) )
parameter_estimates2$SD = apply_epsilon( fit )
}
# Extract standard outputs
if( "par" %in% names(parameter_estimates2) ){
if( !is.null(tmb_list$Obj$env$intern) && tmb_list$Obj$env$intern==TRUE ){
Report = as.list(tmb_list$Obj$env$reportenv)
}else{
Report = tmb_list$Obj$report()
}
ParHat = tmb_list$Obj$env$parList( parameter_estimates2$par )
# Label stuff
Report = amend_output( Report = Report,
TmbData = data_list,
Map = tmb_list$Map,
Sdreport = parameter_estimates2$SD,
year_labels = year_labels,
category_names = category_names,
extrapolation_list = extrapolation_list )
}else{
Report = ParHat = "Model is not converged"
}
# Build and output
input_args = list( "extra_args" = extra_args,
"extrapolation_args_input" = extrapolation_args_input,
"model_args_input" = model_args_input,
"spatial_args_input" = spatial_args_input,
"optimize_args_input1" = optimize_args_input1,
"optimize_args_input2" = optimize_args_input2,
"data_args_input" = data_args_input )
Return = list( "data_frame" = data_frame,
"extrapolation_list" = extrapolation_list,
"spatial_list" = spatial_list,
"data_list" = data_list,
"tmb_list" = tmb_list,
"parameter_estimates" = parameter_estimates2,
"Report" = Report,
"ParHat" = ParHat,
"year_labels" = year_labels,
"years_to_plot" = years_to_plot,
"category_names" = category_names,
"settings" = settings,
"input_args" = input_args,
"X1config_cp" = X1config_cp,
"X2config_cp" = X2config_cp,
"covariate_data" = covariate_data,
"X1_formula" = X1_formula,
"X2_formula" = X2_formula,
"Q1config_k" = Q1config_k,
"Q2config_k" = Q1config_k,
"catchability_data" = catchability_data,
"Q1_formula" = Q1_formula,
"Q2_formula" = Q2_formula,
"total_time" = Sys.time() - start_time )
# Add stuff for effects package
Return$effects = list()
if( !is.null(catchability_data) ){
catchability_data_full = data.frame( catchability_data, "linear_predictor"=0 )
Q1_formula_full = update.formula(Q1_formula, linear_predictor~.+0)
call_Q1 = lm( Q1_formula_full, data=catchability_data_full)$call
Q2_formula_full = update.formula(Q2_formula, linear_predictor~.+0)
call_Q2 = lm( Q2_formula_full, data=catchability_data_full)$call
Return$effects = c( Return$effects, list(call_Q1=call_Q1, call_Q2=call_Q2, catchability_data_full=catchability_data_full) )
}
if( !is.null(covariate_data) ){
covariate_data_full = data.frame( covariate_data, "linear_predictor"=0 )
X1_formula_full = update.formula(X1_formula, linear_predictor~.+0)
call_X1 = lm( X1_formula_full, data=covariate_data_full)$call
X2_formula_full = update.formula(X2_formula, linear_predictor~.+0)
call_X2 = lm( X2_formula_full, data=covariate_data_full)$call
Return$effects = c( Return$effects, list(call_X1=call_X1, call_X2=call_X2, covariate_data_full=covariate_data_full) )
}
# Add stuff for marginaleffects package
Return$last.par.best = tmb_list$Obj$env$last.par.best
# class and return
class(Return) = "fit_model"
return( Return )
}
#' Print parameter estimates and standard errors.
#'
#' @title Print parameter estimates
#' @param x Output from \code{\link{fit_model}}
#' @param ... Not used
#' @return NULL
#' @method print fit_model
#' @export
print.fit_model <- function(x, ...)
{
cat("fit_model(.) result\n")
if( "parameter_estimates" %in% names(x) ){
print( x$parameter_estimates )
}else{
cat("`parameter_estimates` not available in `fit_model`\n")
}
invisible(x$parameter_estimates)
}
#' Print parameter estimates and standard errors.
#'
#' @title Print parameter estimates
#' @param fit Output from \code{\link{fit_model}}
#' @param what String specifying what elements of results to plot; options include `extrapolation_grid`, `spatial_mesh`, and `results`
#' @param ... Arguments passed to \code{\link{plot_results}}
#' @return NULL
#' @method plot fit_model
#' @export
plot.fit_model <- function(x, what="results", ...)
{
if(!is.character(what)) stop("Check `what` in `plot.fit_model`")
## Plot extrapolation-grid
if( length(grep(what, "extrapolation_grid")) ){
cat("\n### Running `plot.make_extrapolation_info`\n")
plot( x$extrapolation_list )
return(invisible(NULL))
}
## Plot extrapolation-grid
if( length(grep(what, c("spatial_info","inla_mesh"))) ){
cat("\n### Running `plot.make_spatial_info`\n")
plot( x$spatial_list )
return(invisible(NULL))
}
# diagnostic plots
if( length(grep(what, "results")) ){
cat("\n### Running `plot_results`\n")
ans = plot_results( x, ... )
return(invisible(ans))
}
stop( "input `what` not matching available options" )
}
#' Extract summary of spatial estimates
#'
#' \code{summary.fit_model} extracts commonly used quantities derived from a fitted VAST model
#'
#' \code{summary.fit_model} faciliates common queries for model output including:
#' \itemize{
#' \item \code{what="density"} returns a tagged list containing element \code{Density_dataframe},
#' which lists the estimated density for every Latitude-Longitude-Year-Category combination
#' for every modelled location in the extrapolation-grid.
#' \item \code{what="residuals"} returns a DHARMa object containing PIT residuals;
#' See details section for more information.
#' }
#'
#' For calculating residuals, the function calls package \code{\link[DHARMa]{DHARMa}}
#' to create a diagnostic object for simulation-based quantile residuals.
#' It specifically simulates replicated data sets from the predictive distribution of data
#' conditional on estimated fixed and random effects. It then
#' calculates probability-integral-transform (PIT) residuals from the observed and simulated values.
#' It then replaces the automatically calculated residuals in the DHARMa object with these these PIT residuals,
#' so that DHARMa can be used to plot those PIT residuals. PIT residuals are used because the original DHARMa calculations
#' are not correct when using a delta-model (due to additional jittered values added by DHARMa when detecting multiple 0-valued observations), hence
#' the need to call this function to correctly calculate PIT residuals for a delta-model.
#'
#' Note that \code{summary(fit, ..., type=0} uses \code{\link{oneStepPredict_deltaModel}} to calculate one-step-ahead
#' residuals. These are probably the most appropriate method for evaluating residuals, but are also *very* slow to calculate relative
#' to other methods.
#'
#' @inheritParams simulate_data
#' @inheritParams DHARMa::plotResiduals
#'
#' @param x Output from \code{\link{fit_model}}
#' @param what String indicating what to summarize; options are `density`, `index` or `residuals`
#' @param n_samples Number of samples used when \code{what="residuals"}
#' @param ... additional arguments passed to \code{\link[DHARMa]{plotResiduals}} when \code{what="residuals"}
#'
#' @return NULL
#' @seealso \code{\link{plot_quantile_residuals}} to plot output of \code{summary.fit_model(x,what="residuals")}
#' @method summary fit_model
#' @export
summary.fit_model <-
function( x,
what = "density",
n_samples = 250,
working_dir = NULL,
type = 1,
random_seed = NULL,
form = NULL,
category_names = x$category_names,
year_labels = x$year_labels,
...)
{
ans = NULL
# Check and implement units and labels
x$Report = amend_output( fit = x,
year_labels = year_labels,
category_names = category_names )
if( tolower(what) %in% c("density","index") ){
# Load location of extrapolation-grid
ans[["extrapolation_grid"]] = print( x$extrapolation_list, quiet=TRUE )
# Load density estimates
if( tolower(what) == "density" ){
ans[["Density_array"]] = x$Report[["D_gct"]]
}else if( tolower(what) == "index" ){
ans[["Density_array"]] = x$Report[["Index_gctl"]]
}
# Exclude boundary knots
if( !( x$settings$fine_scale==TRUE | x$spatial_list$Method=="Stream_network" ) ){
index_tmp = x$spatial_list$NN_Extrap$nn.idx[ which(x$extrapolation_list[["Area_km2_x"]]>0), 1 ]
ans[["Density_array"]] = ans[["Density_array"]][ index_tmp,,,drop=FALSE]
}
# Error check
if( any(sapply(dimnames(ans[["Density_array"]]),FUN=is.null)) ){
stop("`summary.fit_model` assumes that arrays are labeled")
}
# Expand as grid
Density_dataframe = expand.grid( dimnames(ans[["Density_array"]]) )
Density_dataframe = cbind( Density_dataframe, ans[["extrapolation_grid"]][Density_dataframe[,'Site'],], as.vector(ans[["Density_array"]]) )
colnames(Density_dataframe)[ncol(Density_dataframe)] = tolower(what)
# Save output
ans[["Density_dataframe"]] = Density_dataframe
ans[['year_labels']] = x[['year_labels']]
rownames(Density_dataframe) = NULL
# Print to terminal
cat("\n### Printing head of and tail `Density_dataframe`, and returning data frame in output object\n")
print(head(Density_dataframe))
print(tail(Density_dataframe))
}
# Residuals
if( tolower(what) == "residuals" ){
# extract objects
Obj = x$tmb_list$Obj
# Change n_g
# Must change back explicitly because TMB appears to pass env as a pointer, so changes in copy affect original x outside of function!
n_g_orig = Obj$env$data$n_g
revert_settings = function(n_g){Obj$env$data$n_g = n_g}
on.exit( revert_settings(n_g_orig) )
Obj$env$data$n_g = 0
if( type %in% c(1,4) ){
b_iz = matrix(NA, nrow=length(x$data_list$b_i), ncol=n_samples)
message( "Sampling from the distribution of data conditional on estimated fixed and random effects" )
for( zI in 1:n_samples ){
if( zI%%max(1,floor(n_samples/10)) == 0 ){
message( " Finished sample ", zI, " of ",n_samples )
}
b_iz[,zI] = simulate_data( fit=list(tmb_list=list(Obj=Obj)), type=type, random_seed=list(random_seed+zI,NULL)[[1+is.null(random_seed)]] )$b_i
}
#if( any(is.na(x$data_list$b_i)) ){
# stop("dharmaRes not designed to work when any observations have b_i=NA")
#}
# Substitute any observation where b_i = NA with all zeros, which will then have a uniform PIT
which_na = which(is.na(x$data_list$b_i))
if( length(which_na) > 0 ){
x$data_list$b_i[which_na] = 0
b_iz[which_na,] = 0
warning("When calculating DHARMa residuals, replacing instances where b_i=NA with a uniform PIT residual")
}
if( any(is.na(b_iz)) ){
stop("Check simulated residuals for NA values")
}
# Run DHARMa
# Adding jitters because DHARMa version 0.3.2.0 sometimes still throws an error method="traditional" and integer=FALSE without jitters
dharmaRes = DHARMa::createDHARMa( simulatedResponse = strip_units(b_iz) + 1e-10*array(rnorm(prod(dim(b_iz))),dim=dim(b_iz)),
observedResponse = strip_units(x$data_list$b_i) + 1e-10*rnorm(length(x$data_list$b_i)),
fittedPredictedResponse = strip_units(x$Report$D_i),
integer = FALSE)
#dharmaRes = DHARMa::createDHARMa(simulatedResponse=strip_units(b_iz),
# observedResponse=strip_units(x$data_list$b_i),
# fittedPredictedResponse=strip_units(x$Report$D_i),
# method="PIT")
# Save to report error
if( FALSE ){
all = list( simulatedResponse=strip_units(b_iz), observedResponse=strip_units(x$data_list$b_i), fittedPredictedResponse=strip_units(x$Report$D_i) )
#save(all, file=paste0(root_dir,"all.RData") )
dharmaRes = DHARMa::createDHARMa(simulatedResponse=all$simulatedResponse + rep(1,nrow(all$simulatedResponse))%o%c(0.001*rnorm(1),rep(0,ncol(all$simulatedResponse)-1)),
observedResponse=all$observedResponse,
fittedPredictedResponse=all$fittedPredictedResponse,
method="PIT")
}
# Calculate probability-integral-transform (PIT) residuals
message( "Substituting probability-integral-transform (PIT) residuals for DHARMa-calculated residuals" )
prop_lessthan_i = apply( b_iz<outer(x$data_list$b_i,rep(1,n_samples)),
MARGIN=1,
FUN=mean )
prop_lessthanorequalto_i = apply( b_iz<=outer(x$data_list$b_i,rep(1,n_samples)),
MARGIN=1,
FUN=mean )
PIT_i = runif(min=prop_lessthan_i, max=prop_lessthanorequalto_i, n=length(prop_lessthan_i) )
# cbind( "Difference"=dharmaRes$scaledResiduals - PIT_i, "PIT"=PIT_i, "Original"=dharmaRes$scaledResiduals, "b_i"=x$data_list$b_i )
dharmaRes$scaledResiduals = PIT_i
}else if( type==0 ){
# Check for issues
if( !all(x$data_list$ObsModel_ez[,1] %in% c(1,2)) ){
stop("oneStepAhead residuals only code for gamma and lognormal distributions")
}
# Run OSA
message( "Running oneStepPredict_deltaModel for each observation, to then load them into DHARMa object for plotting" )
osa = oneStepPredict_deltaModel( obj = x$tmb_list$Obj,
observation.name = "b_i",
method = "cdf",
data.term.indicator = "keep",
deltaSupport = 0,
trace = TRUE )
# Build DHARMa object on fake inputs and load OSA into DHARMa object
dharmaRes = DHARMa::createDHARMa(simulatedResponse=matrix(rnorm(x$data_list$n_i*10,mean=x$data_list$b_i),ncol=10),
observedResponse=x$data_list$b_i,
fittedPredictedResponse=x$Report$D_i,
integer=FALSE)
dharmaRes$scaledResiduals = pnorm(osa$residual)
}else{
stop("`type` only makes sense for 0 (oneStepAhead), 1 (conditional, a.k.a. measurement error) or 4 (unconditional) simulations")
}
# do plot
if( is.null(working_dir) ){
plot_dharma(dharmaRes, ...)
}else if(!is.na(working_dir) ){
png(file=file.path(working_dir,"quantile_residuals.png"), width=8, height=4, res=200, units='in')
plot_dharma(dharmaRes, form=form, ...)
dev.off()
}
# Return stuff
ans = dharmaRes
message( "Invisibly returning output from `DHARMa::createDHARMa`, e.g., to apply `plot.DHARMa` to this output")
}
if( tolower(what) %in% c("parhat","estimates") ){
ans[["estimates"]] = x$ParHat
cat("\n### Printing slots of `ParHat`, and returning list in output object")
print(names(x$ParHat))
}
if( is.null(ans) ){
stop( "`summary.fit_model` not implemented for inputted value of argument `what`" )
}
# diagnostic plots
return(invisible(ans))
}
#' Predict density for new samples (\emph{Beta version; may change without notice})
#'
#' \code{predict.fit_model} calculates predictions given new data
#'
#' \code{predict.fit_model} is designed with two purposes in mind:
#' \enumerate{
#' \item If \code{new_covariate_data=NULL} as by default, then the model uses the covariate values supplied during original model fits,
#' and interpolates as needed from those supplied values to new predicted locations. This then uses *exactly* the same information
#' as was available during model fitting.
#' \item If \code{new_covariate_data} is supplied with new values (e.g., at locations for predictions), then these values are used in
#' combination with original covariate values when interpolating to new values. However, supplying \code{new_oovariate_data}
#' at the same Lat-Lon-Year combination as any original covariate value will delete those matches in the latter, such that originally fitted data
#' can be predicted using alternative values for covariates (e.g., when calculating partial dependence plots)
#' }
#'
#' @inheritParams make_covariates
#' @inheritParams make_data
#' @param x Output from \code{\link{fit_model}}
#' @param what Which output from \code{fit$Report} should be extracted; default is predicted density
#' @param keep_old_covariates Whether to add new_covariate_data to existing data.
#' This is useful when predicting values at new locations, but does not work
#' when predicting data are locations with existing data (because the interpolation of
#' covariate values will conflict for existing and new covariate values), e.g.,
#' when calculating partial dependence plots for existing data.
#'
#' @return NULL
#'
#' @examples
#' \dontrun{
#'
#' # Showing use of package pdp for partial dependence plots
#' pred.fun = function( object, newdata ){
#' predict( x=object,
#' Lat_i = object$data_frame$Lat_i,
#' Lon_i = object$data_frame$Lon_i,
#' t_i = object$data_frame$t_i,
#' a_i = object$data_frame$a_i,
#' what = "P1_iz",
#' new_covariate_data = newdata,
#' do_checks = FALSE )
#' }
#'
#' library(ggplot2)
#' library(pdp)
#' Partial = partial( object = fit,
#' pred.var = "BOT_DEPTH",
#' pred.fun = pred.fun,
#' train = fit$covariate_data )
#' autoplot(Partial)
#'
#' }
#'
#' @method predict fit_model
#' @export
predict.fit_model <- function(x,
what="D_i",
Lat_i,
Lon_i,
t_i,
a_i,
c_iz = rep(0,length(t_i)),
v_i = rep(0,length(t_i)),
new_covariate_data = NULL,
new_catchability_data = NULL,
do_checks = TRUE,
working_dir = getwd() )
{
message("`predict.fit_model(.)` is in beta-testing, and please explore results carefully prior to using")
# Check issues
if( !(what%in%names(x$Report)) || (length(x$Report[[what]])!=x$data_list$n_i) ){
stop("`what` can only take a few options")
}
if( !is.null(new_covariate_data) ){
# Confirm all columns are available
if( !all(colnames(x$covariate_data) %in% colnames(new_covariate_data)) ){
stop("Please ensure that all columns of `x$covariate_data` are present in `new_covariate_data`")
}
# Eliminate unnecessary columns
new_covariate_data = new_covariate_data[,match(colnames(x$covariate_data),colnames(new_covariate_data))]
# Eliminate old-covariates that are also present in new_covariate_data
NN = RANN::nn2( query=x$covariate_data[,c('Lat','Lon','Year')], data=new_covariate_data[,c('Lat','Lon','Year')], k=1 )
if( any(NN$nn.dist==0) ){
x$covariate_data = x$covariate_data[-which(NN$nn.dist==0),,drop=FALSE]
}
}
if( !is.null(new_catchability_data) ){
stop("Option not implemented")
}
# Process covariates
catchability_data = rbind( x$catchability_data, new_catchability_data )
covariate_data = rbind( x$covariate_data, new_covariate_data )
# Process inputs
PredTF_i = c( x$data_list$PredTF_i, rep(1,length(t_i)) )
b_i = c( x$data_frame[,"b_i"], rep(1,length(t_i)) )
c_iz = rbind( matrix(x$data_frame[,grep("c_iz",names(x$data_frame))]), matrix(c_iz) )
Lat_i = c( x$data_frame[,"Lat_i"], Lat_i )
Lon_i = c( x$data_frame[,"Lon_i"], Lon_i )
a_i = c( x$data_frame[,"a_i"], a_i )
v_i = c( x$data_frame[,"v_i"], v_i )
t_i = c( x$data_frame[,"t_i"], t_i )
#assign("b_i", b_i, envir=.GlobalEnv)
# Build information regarding spatial location and correlation
message("\n### Re-making spatial information")
spatial_args_new = list("anisotropic_mesh"=x$spatial_list$MeshList$anisotropic_mesh, "Kmeans"=x$spatial_list$Kmeans,
"Lon_i"=Lon_i, "Lat_i"=Lat_i )
spatial_args_input = combine_lists( input=spatial_args_new, default=x$input_args$spatial_args_input )
spatial_list = do.call( what=make_spatial_info, args=spatial_args_input )
# Check spatial_list
if( !all.equal(spatial_list$MeshList,x$spatial_list$MeshList) ){
stop("`MeshList` generated during `predict.fit_model` doesn't match that of original fit; please email package author to report issue")
}
# Build data
# Do *not* restrict inputs to formalArgs(make_data) because other potential inputs are still parsed by make_data for backwards compatibility
message("\n### Re-making data object")
data_args_new = list( "c_iz"=c_iz, "b_i"=b_i, "a_i"=a_i, "v_i"=v_i, "PredTF_i"=PredTF_i,
"t_i"=t_i, "spatial_list"=spatial_list,
"covariate_data"=covariate_data, "catchability_data"=catchability_data )
data_args_input = combine_lists( input=data_args_new, default=x$input_args$data_args_input ) # Do *not* use args_to_use
data_list = do.call( what=make_data, args=data_args_input )
data_list$n_g = 0
# Build object
message("\n### Re-making TMB object")
model_args_default = list("TmbData"=data_list, "RunDir"=working_dir, "Version"=x$settings$Version,
"RhoConfig"=x$settings$RhoConfig, "loc_x"=spatial_list$loc_x, "Method"=spatial_list$Method)
model_args_input = combine_lists( input=list("Parameters"=x$ParHat),
default=model_args_default, args_to_use=formalArgs(make_model) )
tmb_list = do.call( what=make_model, args=model_args_input )
# Extract output
Report = tmb_list$Obj$report()
Y_i = Report[[what]][(1+nrow(x$data_frame)):length(Report$D_i)]
# sanity check
#if( all.equal(covariate_data,x$covariate_data) & Report$jnll!=x$Report$jnll){
if( do_checks==TRUE && (Report$jnll!=x$Report$jnll) ){
message("Problem detected in `predict.fit_model`; returning outputs for diagnostic purposes")
Return = list("Report"=Report, "data_list"=data_list)
return(Return)
}
# return prediction
return(Y_i)
}
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