Nothing
R/15.1-analysis-extraction.R
In fb4package: 'Fish Bioenergetics 4.0' Model Implementation with High-Performance 'TMB' Backend
Defines functions
create_result_summary
analyze_feeding_performance
analyze_energy_budget
analyze_growth_patterns
Documented in
analyze_energy_budget
analyze_feeding_performance
analyze_growth_patterns
create_result_summary
#' Basic Analysis and Extraction Functions for FB4 Results
#'
#' @description
#' Functions for basic analysis and extraction of FB4 simulation results.
#' These functions build on the core extraction functions to provide
#' meaningful biological interpretations and statistical summaries.
#' Exported functions include \code{analyze_growth_patterns},
#' \code{analyze_energy_budget}, \code{analyze_feeding_performance},
#' and \code{create_result_summary}.
#'
#' @references
#' Deslauriers, D., Chipps, S.R., Breck, J.E., Rice, J.A. and Madenjian, C.P.
#' (2017). Fish Bioenergetics 4.0: An R-based modeling application.
#' \emph{Fisheries}, 42(11), 586–596. \doi{10.1080/03632415.2017.1377558}
#'
#' @return No return value; this page documents the result extraction and summary functions. See individual function documentation for return values.
#' @name analysis-extraction
#' @aliases analysis-extraction
NULL
# ============================================================================
# GROWTH ANALYSIS FUNCTIONS
# ============================================================================
#' Analyze growth patterns from FB4 results
#'
#' @description
#' Extracts and analyzes growth patterns from FB4 simulation results.
#' Calculates growth rates, efficiency metrics, and provides uncertainty
#' estimates when available.
#'
#' @param result FB4 result object
#' @param individual_id Individual ID for hierarchical models (NULL for population/single individual)
#' @param confidence_level Confidence level for intervals (default 0.95)
#' @return A named list with at minimum \code{method} (character),
#' \code{has_uncertainty} (logical), \code{individual_id}, and
#' \code{initial_weight} (numeric, g). The following growth metrics are
#' included as sub-lists each with \code{estimate}, \code{se}, \code{ci_lower},
#' and \code{ci_upper}: \code{final_weight} (g), \code{total_growth} (g),
#' and \code{relative_growth} (\%). When simulation duration is available,
#' \code{daily_growth_rate} (g/day) and \code{specific_growth_rate}
#' (\%/day) are appended. For fitted methods a \code{p_value} sub-list
#' (\code{estimate}, \code{se}) is also included; for hierarchical
#' population-level calls, \code{n_individuals} (integer) is added.
#' @export
#'
#' @examples
#' \donttest{
#' data(fish4_parameters)
#' sp <- fish4_parameters[["Oncorhynchus tshawytscha"]]$life_stages$adult
#' info <- fish4_parameters[["Oncorhynchus tshawytscha"]]$species_info
#' bio <- Bioenergetic(
#' species_params = sp,
#' species_info = info,
#' environmental_data = list(
#' temperature = data.frame(Day = 1:30, Temperature = rep(12, 30))
#' ),
#' diet_data = list(
#' proportions = data.frame(Day = 1:30, Prey1 = 1.0),
#' energies = data.frame(Day = 1:30, Prey1 = 5000),
#' prey_names = "Prey1"
#' ),
#' simulation_settings = list(initial_weight = 100, duration = 30)
#' )
#' bio$species_params$predator$ED_ini <- 5000
#' bio$species_params$predator$ED_end <- 5500
#' result <- run_fb4(bio, strategy = "direct", p_value = 0.5, verbose = FALSE)
#' growth <- analyze_growth_patterns(result)
#' }
analyze_growth_patterns <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
result_type <- detect_result_type(result)
method <- result_type$method
has_uncertainty <- result_type$has_uncertainty
# Get basic growth information
growth_info <- list(
method = method,
has_uncertainty = has_uncertainty,
individual_id = individual_id
)
# Extract growth metrics based on method
if (method == "hierarchical") {
if (is.null(individual_id)) {
# Population mean
pop_results <- get_population_results(result, confidence_level)
growth_info$initial_weight <- result$summary$initial_weight %||% NA
growth_info$final_weight <- list(
estimate = pop_results$mean_final_weight_est %||% NA,
se = pop_results$mean_final_weight_se %||% NA,
ci_lower = pop_results$mean_final_weight_ci_lower %||% NA,
ci_upper = pop_results$mean_final_weight_ci_upper %||% NA
)
growth_info$total_growth <- list(
estimate = pop_results$mean_total_growth_est %||% NA,
se = pop_results$mean_total_growth_se %||% NA,
ci_lower = pop_results$mean_total_growth_ci_lower %||% NA,
ci_upper = pop_results$mean_total_growth_ci_upper %||% NA
)
growth_info$relative_growth <- list(
estimate = pop_results$mean_relative_growth_est %||% NA,
se = pop_results$mean_relative_growth_se %||% NA,
ci_lower = pop_results$mean_relative_growth_ci_lower %||% NA,
ci_upper = pop_results$mean_relative_growth_ci_upper %||% NA
)
growth_info$n_individuals <- pop_results$n_individuals
} else {
# Individual result
ind_results <- get_individual_results(result, confidence_level)
if (individual_id > nrow(ind_results)) {
stop("Individual ID ", individual_id, " not found. Available: 1-", nrow(ind_results))
}
ind_data <- ind_results[individual_id, ]
growth_info$initial_weight <- result$summary$initial_weight %||% NA # Same for all individuals
growth_info$final_weight <- list(
estimate = ind_data$final_weight_est %||% NA,
se = ind_data$final_weight_se %||% NA,
ci_lower = ind_data$final_weight_ci_lower %||% NA,
ci_upper = ind_data$final_weight_ci_upper %||% NA
)
growth_info$total_growth <- list(
estimate = ind_data$total_growth_est %||% NA,
se = ind_data$total_growth_se %||% NA,
ci_lower = ind_data$total_growth_ci_lower %||% NA,
ci_upper = ind_data$total_growth_ci_upper %||% NA
)
growth_info$relative_growth <- list(
estimate = ind_data$relative_growth_est %||% NA,
se = ind_data$relative_growth_se %||% NA,
ci_lower = ind_data$relative_growth_ci_lower %||% NA,
ci_upper = ind_data$relative_growth_ci_upper %||% NA
)
growth_info$p_value <- list(
estimate = ind_data$p_estimate %||% NA,
se = ind_data$p_se %||% NA
)
}
} else {
# Non-hierarchical methods
growth_info$initial_weight <- result$summary$initial_weight %||% NA
if (method == "mle" && result$model_info$backend == "tmb") {
# MLE with TMB - has uncertainty
tmb_unc <- result$method_data$tmb_uncertainty
growth_info$final_weight <- list(
estimate = tmb_unc$final_weight_est %||% result$summary$predicted_weight %||% NA,
se = tmb_unc$final_weight_se %||% NA,
ci_lower = NA,
ci_upper = NA
)
growth_info$total_growth <- list(
estimate = tmb_unc$total_growth_est %||% NA,
se = tmb_unc$total_growth_se %||% NA,
ci_lower = NA,
ci_upper = NA
)
growth_info$relative_growth <- list(
estimate = tmb_unc$relative_growth_est %||% NA,
se = tmb_unc$relative_growth_se %||% NA,
ci_lower = NA,
ci_upper = NA
)
# Calculate CIs if we have estimates and SEs
z <- z_score(confidence_level)
for (metric in c("final_weight", "total_growth", "relative_growth")) {
est <- growth_info[[metric]]$estimate
se <- growth_info[[metric]]$se
if (!is.na(est) && !is.na(se)) {
growth_info[[metric]]$ci_lower <- est - z * se
growth_info[[metric]]$ci_upper <- est + z * se
}
}
} else {
# Traditional methods or MLE without full uncertainty
final_weight <- result$summary$final_weight %||% result$summary$predicted_weight %||% NA
growth_info$final_weight <- list(
estimate = final_weight,
se = NA,
ci_lower = NA,
ci_upper = NA
)
if (!is.na(final_weight) && !is.na(growth_info$initial_weight)) {
total_growth <- final_weight - growth_info$initial_weight
relative_growth <- (final_weight / growth_info$initial_weight - 1) * 100
growth_info$total_growth <- list(
estimate = total_growth,
se = NA,
ci_lower = NA,
ci_upper = NA
)
growth_info$relative_growth <- list(
estimate = relative_growth,
se = NA,
ci_lower = NA,
ci_upper = NA
)
}
}
# Add p_value information for fitted methods
if (method %in% c("mle", "binary_search", "optim")) {
growth_info$p_value <- list(
estimate = result$summary$p_estimate %||% result$summary$p_value %||% NA,
se = result$method_data$sigma_se %||% NA
)
}
}
# Calculate derived metrics
duration <- result$summary$simulation_days %||% NA
if (!is.na(duration) && !is.na(growth_info$total_growth$estimate)) {
# Daily growth rate (absolute)
growth_info$daily_growth_rate <- list(
estimate = growth_info$total_growth$estimate / duration,
se = if (!is.na(growth_info$total_growth$se)) growth_info$total_growth$se / duration else NA,
ci_lower = if (!is.na(growth_info$total_growth$ci_lower)) growth_info$total_growth$ci_lower / duration else NA,
ci_upper = if (!is.na(growth_info$total_growth$ci_upper)) growth_info$total_growth$ci_upper / duration else NA
)
# Specific growth rate (% per day)
if (!is.na(growth_info$initial_weight) && !is.na(growth_info$final_weight$estimate)) {
sgr <- log(growth_info$final_weight$estimate / growth_info$initial_weight) / duration * 100
growth_info$specific_growth_rate <- list(
estimate = sgr,
se = NA, # Would need delta method for proper SE
ci_lower = NA,
ci_upper = NA
)
}
}
return(growth_info)
}
# ============================================================================
# ENERGY BUDGET ANALYSIS FUNCTIONS
# ============================================================================
#' Analyze energy budget from FB4 results
#'
#' @description
#' Analyzes energy budget components from FB4 simulation results.
#' Calculates proportional allocation to different processes with
#' uncertainty propagation when available.
#'
#' @param result FB4 result object
#' @param individual_id Individual ID for hierarchical models (NULL for population/single individual)
#' @param confidence_level Confidence level for intervals (default 0.95)
#' @return A named list with four elements:
#' \describe{
#' \item{energy_components}{The list returned by
#' \code{\link{get_energy_budget_uncertainty}}, containing six component
#' sub-lists each with \code{estimate}, \code{se}, \code{ci_lower}, and
#' \code{ci_upper}.}
#' \item{proportions}{Named list of proportional allocations
#' (\code{prop_respiration}, \code{prop_egestion}, \code{prop_excretion},
#' \code{prop_sda}, \code{prop_net}), each a sub-list with \code{estimate},
#' \code{se}, \code{ci_lower}, and \code{ci_upper}. \code{NULL} when
#' consumption energy is zero or unavailable.}
#' \item{summary_metrics}{Named list with \code{gross_growth_efficiency},
#' \code{metabolic_scope}, and \code{assimilation_efficiency} sub-lists
#' (each \code{estimate} + \code{se}). \code{NULL} when proportions are
#' unavailable.}
#' \item{balance_check}{Named list with \code{consumption_energy},
#' \code{total_allocated}, \code{balance_error}, and
#' \code{relative_error} (all numeric) to verify mass-balance closure.}
#' }
#' Plus the context scalars \code{method}, \code{has_uncertainty}, and
#' \code{individual_id}.
#' @export
#' @examples
#' \donttest{
#' data(fish4_parameters)
#' sp <- fish4_parameters[["Oncorhynchus tshawytscha"]]$life_stages$adult
#' info <- fish4_parameters[["Oncorhynchus tshawytscha"]]$species_info
#' bio <- Bioenergetic(
#' species_params = sp,
#' species_info = info,
#' environmental_data = list(
#' temperature = data.frame(Day = 1:30, Temperature = rep(12, 30))
#' ),
#' diet_data = list(
#' proportions = data.frame(Day = 1:30, Prey1 = 1.0),
#' energies = data.frame(Day = 1:30, Prey1 = 5000),
#' prey_names = "Prey1"
#' ),
#' simulation_settings = list(initial_weight = 100, duration = 30)
#' )
#' bio$species_params$predator$ED_ini <- 5000
#' bio$species_params$predator$ED_end <- 5500
#' result <- run_fb4(bio, strategy = "direct", p_value = 0.5, verbose = FALSE)
#' budget <- analyze_energy_budget(result)
#' }
analyze_energy_budget <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
result_type <- detect_result_type(result)
method <- result_type$method
has_uncertainty <- result_type$has_uncertainty
# Get energy budget components
budget <- get_energy_budget_uncertainty(result, individual_id, confidence_level)
# Initialize analysis result
budget_analysis <- list(
method = method,
has_uncertainty = has_uncertainty,
individual_id = individual_id,
energy_components = budget
)
# Calculate energy proportions if consumption energy is available
consumption_est <- budget$consumption_energy$estimate
if (!is.na(consumption_est) && consumption_est > 0) {
# Calculate proportions
components <- c("respiration_energy", "egestion_energy", "excretion_energy", "sda_energy", "net_energy")
budget_analysis$proportions <- list()
for (component in components) {
comp_est <- budget[[component]]$estimate
comp_se <- budget[[component]]$se
if (!is.na(comp_est)) {
prop_est <- comp_est / consumption_est
# Approximate SE for proportion using delta method
prop_se <- NA
if (!is.na(comp_se) && !is.na(budget$consumption_energy$se)) {
# Delta method for ratio: Var(Y/X) ~ (Y/X)^2 * [Var(Y)/Y^2 + Var(X)/X^2 - 2*Cov(X,Y)/(X*Y)]
# Assuming independence: Cov(X,Y) = 0
cv_comp <- comp_se / comp_est
cv_cons <- budget$consumption_energy$se / consumption_est
prop_se <- prop_est * sqrt(cv_comp^2 + cv_cons^2)
}
prop_name <- gsub("_energy", "", component)
budget_analysis$proportions[[paste0("prop_", prop_name)]] <- list(
estimate = prop_est,
se = prop_se,
ci_lower = if (!is.na(prop_se)) prop_est - z_score(confidence_level) * prop_se else NA,
ci_upper = if (!is.na(prop_se)) prop_est + z_score(confidence_level) * prop_se else NA
)
}
}
# Calculate summary metrics
prop_respiration <- budget_analysis$proportions$prop_respiration$estimate %||% 0
prop_sda <- budget_analysis$proportions$prop_sda$estimate %||% 0
prop_net <- budget_analysis$proportions$prop_net$estimate %||% 0
budget_analysis$summary_metrics <- list(
gross_growth_efficiency = list(
estimate = prop_net,
se = budget_analysis$proportions$prop_net$se %||% NA
),
metabolic_scope = list(
estimate = prop_respiration + prop_sda,
se = NA # Would need covariance for proper SE
),
assimilation_efficiency = list(
estimate = 1 - (budget_analysis$proportions$prop_egestion$estimate %||% 0),
se = budget_analysis$proportions$prop_egestion$se %||% NA
)
)
# Energy balance check
total_allocated <- sum(vapply(components, function(x) budget[[x]]$estimate %||% 0, numeric(1)))
budget_analysis$balance_check <- list(
consumption_energy = consumption_est,
total_allocated = total_allocated,
balance_error = abs(total_allocated - consumption_est),
relative_error = abs(total_allocated - consumption_est) / consumption_est * 100
)
} else {
budget_analysis$proportions <- NULL
budget_analysis$summary_metrics <- NULL
budget_analysis$balance_check <- list(
consumption_energy = consumption_est,
error = "Consumption energy not available or zero"
)
}
return(budget_analysis)
}
# ============================================================================
# FEEDING ANALYSIS FUNCTIONS
# ============================================================================
#' Analyze feeding performance from FB4 results
#'
#' @description
#' Analyzes feeding-related metrics including consumption rates,
#' feeding efficiency, and p_value estimates with uncertainty.
#'
#' @param result FB4 result object
#' @param individual_id Individual ID for hierarchical models (NULL for population/single individual)
#' @param confidence_level Confidence level for intervals (default 0.95)
#' @return A named list with at minimum \code{method} (character),
#' \code{has_uncertainty} (logical), and \code{individual_id}. Additional
#' elements present when the relevant data are available:
#' \describe{
#' \item{total_consumption}{The list returned by
#' \code{\link{get_consumption_uncertainty}} (\code{estimate}, \code{se},
#' \code{ci_lower}, \code{ci_upper}, plus context scalars).}
#' \item{daily_consumption}{Sub-list (\code{estimate}, \code{se},
#' \code{ci_lower}, \code{ci_upper}) for the daily consumption rate
#' (g/day).}
#' \item{specific_consumption}{Sub-list (same four slots) for the
#' specific consumption rate (g consumption / g fish / day).}
#' \item{p_value}{Structure depends on method: for hierarchical it contains
#' \code{population_mean}, \code{population_se}, \code{population_sd}, and
#' \code{n_individuals}; for single-individual methods it contains
#' \code{estimate}, \code{se}, \code{ci_lower}, and \code{ci_upper}.}
#' \item{feeding_efficiency}{Sub-list (\code{estimate}, \code{se},
#' \code{ci_lower}, \code{ci_upper}) for the ratio of total growth to
#' total consumption (dimensionless).}
#' }
#' @export
#' @examples
#' \donttest{
#' data(fish4_parameters)
#' sp <- fish4_parameters[["Oncorhynchus tshawytscha"]]$life_stages$adult
#' info <- fish4_parameters[["Oncorhynchus tshawytscha"]]$species_info
#' bio <- Bioenergetic(
#' species_params = sp,
#' species_info = info,
#' environmental_data = list(
#' temperature = data.frame(Day = 1:30, Temperature = rep(12, 30))
#' ),
#' diet_data = list(
#' proportions = data.frame(Day = 1:30, Prey1 = 1.0),
#' energies = data.frame(Day = 1:30, Prey1 = 5000),
#' prey_names = "Prey1"
#' ),
#' simulation_settings = list(initial_weight = 100, duration = 30)
#' )
#' bio$species_params$predator$ED_ini <- 5000
#' bio$species_params$predator$ED_end <- 5500
#' result <- run_fb4(bio, strategy = "direct", p_value = 0.5, verbose = FALSE)
#' feeding <- analyze_feeding_performance(result)
#' }
analyze_feeding_performance <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
result_type <- detect_result_type(result)
method <- result_type$method
# Get consumption data
consumption <- get_consumption_uncertainty(result, individual_id, confidence_level)
# Initialize feeding analysis
feeding_analysis <- list(
method = method,
has_uncertainty = consumption$has_uncertainty,
individual_id = individual_id
)
# Basic consumption metrics
feeding_analysis$total_consumption <- consumption
# Calculate consumption rates
z <- z_score(confidence_level)
initial_weight <- result$summary$initial_weight %||% NA
simulation_days <- result$summary$simulation_days %||% NA
if (!is.na(consumption$estimate) && !is.na(initial_weight) && !is.na(simulation_days)) {
# Daily consumption rate (g/day)
daily_consumption <- consumption$estimate / simulation_days
daily_consumption_se <- if (!is.na(consumption$se)) consumption$se / simulation_days else NA
feeding_analysis$daily_consumption <- list(
estimate = daily_consumption,
se = daily_consumption_se,
ci_lower = if (!is.na(daily_consumption_se)) daily_consumption - z * daily_consumption_se else NA,
ci_upper = if (!is.na(daily_consumption_se)) daily_consumption + z * daily_consumption_se else NA
)
# Specific consumption rate (g consumption / g fish / day)
specific_consumption <- daily_consumption / initial_weight
specific_consumption_se <- if (!is.na(daily_consumption_se)) daily_consumption_se / initial_weight else NA
feeding_analysis$specific_consumption <- list(
estimate = specific_consumption,
se = specific_consumption_se,
ci_lower = if (!is.na(specific_consumption_se)) specific_consumption - z * specific_consumption_se else NA,
ci_upper = if (!is.na(specific_consumption_se)) specific_consumption + z * specific_consumption_se else NA
)
}
# p_value analysis (feeding rate)
if (method == "hierarchical") {
if (is.null(individual_id)) {
# Population parameters
pop_results <- get_population_results(result, confidence_level)
feeding_analysis$p_value <- list(
population_mean = pop_results$mu_p_estimate %||% NA,
population_se = pop_results$mu_p_se %||% NA,
population_sd = pop_results$sigma_p_estimate %||% NA,
n_individuals = pop_results$n_individuals
)
} else {
# Individual p_value
ind_results <- get_individual_results(result, confidence_level)
if (individual_id <= nrow(ind_results)) {
feeding_analysis$p_value <- list(
estimate = ind_results$p_estimate[individual_id] %||% NA,
se = ind_results$p_se[individual_id] %||% NA
)
}
}
} else if (method %in% c("mle", "binary_search", "optim")) {
# Single p_value estimate
feeding_analysis$p_value <- list(
estimate = result$summary$p_estimate %||% result$summary$p_value %||% NA,
se = result$method_data$sigma_se %||% NA,
ci_lower = result$method_data$confidence_intervals$p_ci_lower %||% NA,
ci_upper = result$method_data$confidence_intervals$p_ci_upper %||% NA
)
}
# Feeding efficiency (if growth data available)
growth <- analyze_growth_patterns(result, individual_id, confidence_level)
if (!is.na(growth$total_growth$estimate) && !is.na(consumption$estimate)) {
feeding_efficiency <- growth$total_growth$estimate / consumption$estimate
# Approximate SE using delta method
feeding_efficiency_se <- NA
if (!is.na(growth$total_growth$se) && !is.na(consumption$se)) {
cv_growth <- growth$total_growth$se / growth$total_growth$estimate
cv_consumption <- consumption$se / consumption$estimate
feeding_efficiency_se <- feeding_efficiency * sqrt(cv_growth^2 + cv_consumption^2)
}
feeding_analysis$feeding_efficiency <- list(
estimate = feeding_efficiency,
se = feeding_efficiency_se,
ci_lower = if (!is.na(feeding_efficiency_se)) feeding_efficiency - z * feeding_efficiency_se else NA,
ci_upper = if (!is.na(feeding_efficiency_se)) feeding_efficiency + z * feeding_efficiency_se else NA
)
}
return(feeding_analysis)
}
# ============================================================================
# SUMMARY STATISTICS FUNCTIONS
# ============================================================================
#' Comprehensive post-simulation analysis summary
#'
#' @description
#' **Post-hoc analysis function** — takes a finished \code{fb4_result} object
#' and bundles growth, feeding, and energy-budget analyses into a single list.
#' Useful when you need all major metrics in one call.
#'
#' This is different from the internal \code{$summary} slot (built automatically
#' during result construction by \code{create_unified_summary()}). This function
#' re-derives richer metrics from \code{daily_output} and supports uncertainty
#' propagation for MLE / bootstrap / hierarchical results.
#'
#' @param result An \code{fb4_result} object returned by \code{run_fb4()}
#' @param individual_id For hierarchical models: individual ID to extract
#' (\code{NULL} returns population-level summary)
#' @param confidence_level Confidence level for uncertainty intervals, default 0.95
#' @return Named list with \code{model_info}, \code{growth}, \code{feeding},
#' \code{energy_budget}, and \code{model_fit} sections
#' @seealso \code{\link{analyze_growth_patterns}}, \code{\link{analyze_feeding_performance}},
#' \code{\link{analyze_energy_budget}}
#' @export
create_result_summary <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
# Get all major analyses
growth <- analyze_growth_patterns(result, individual_id, confidence_level)
feeding <- analyze_feeding_performance(result, individual_id, confidence_level)
budget <- analyze_energy_budget(result, individual_id, confidence_level)
summary_result <- list(
# Model information
model_info = list(
method = result$summary$method,
backend = result$model_info$backend,
has_uncertainty = growth$has_uncertainty,
individual_id = individual_id,
confidence_level = confidence_level
),
# Growth metrics
growth = list(
initial_weight = growth$initial_weight,
final_weight = growth$final_weight,
total_growth = growth$total_growth,
relative_growth = growth$relative_growth,
daily_growth_rate = growth$daily_growth_rate,
specific_growth_rate = growth$specific_growth_rate
),
# Feeding metrics
feeding = list(
total_consumption = feeding$total_consumption,
daily_consumption = feeding$daily_consumption,
specific_consumption = feeding$specific_consumption,
p_value = feeding$p_value,
feeding_efficiency = feeding$feeding_efficiency
),
# Energy budget
energy_budget = list(
components = budget$energy_components,
proportions = budget$proportions,
summary_metrics = budget$summary_metrics,
balance_check = budget$balance_check
),
# Model fit (for statistical methods)
model_fit = if (result$summary$method %in% c("mle", "hierarchical")) {
list(
converged = result$summary$converged,
log_likelihood = result$method_data$model_fit$log_likelihood %||% result$method_data$log_likelihood,
aic = result$method_data$model_fit$aic %||% result$method_data$aic,
bic = result$method_data$model_fit$bic %||% result$method_data$bic
)
} else {
list(
converged = result$summary$converged %||% TRUE
)
}
)
return(summary_result)
}
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Defines functions create_result_summary analyze_feeding_performance analyze_energy_budget analyze_growth_patterns
Documented in analyze_energy_budget analyze_feeding_performance analyze_growth_patterns create_result_summary
#' Basic Analysis and Extraction Functions for FB4 Results
#'
#' @description
#' Functions for basic analysis and extraction of FB4 simulation results.
#' These functions build on the core extraction functions to provide
#' meaningful biological interpretations and statistical summaries.
#' Exported functions include \code{analyze_growth_patterns},
#' \code{analyze_energy_budget}, \code{analyze_feeding_performance},
#' and \code{create_result_summary}.
#'
#' @references
#' Deslauriers, D., Chipps, S.R., Breck, J.E., Rice, J.A. and Madenjian, C.P.
#' (2017). Fish Bioenergetics 4.0: An R-based modeling application.
#' \emph{Fisheries}, 42(11), 586–596. \doi{10.1080/03632415.2017.1377558}
#'
#' @return No return value; this page documents the result extraction and summary functions. See individual function documentation for return values.
#' @name analysis-extraction
#' @aliases analysis-extraction
NULL
# ============================================================================
# GROWTH ANALYSIS FUNCTIONS
# ============================================================================
#' Analyze growth patterns from FB4 results
#'
#' @description
#' Extracts and analyzes growth patterns from FB4 simulation results.
#' Calculates growth rates, efficiency metrics, and provides uncertainty
#' estimates when available.
#'
#' @param result FB4 result object
#' @param individual_id Individual ID for hierarchical models (NULL for population/single individual)
#' @param confidence_level Confidence level for intervals (default 0.95)
#' @return A named list with at minimum \code{method} (character),
#' \code{has_uncertainty} (logical), \code{individual_id}, and
#' \code{initial_weight} (numeric, g). The following growth metrics are
#' included as sub-lists each with \code{estimate}, \code{se}, \code{ci_lower},
#' and \code{ci_upper}: \code{final_weight} (g), \code{total_growth} (g),
#' and \code{relative_growth} (\%). When simulation duration is available,
#' \code{daily_growth_rate} (g/day) and \code{specific_growth_rate}
#' (\%/day) are appended. For fitted methods a \code{p_value} sub-list
#' (\code{estimate}, \code{se}) is also included; for hierarchical
#' population-level calls, \code{n_individuals} (integer) is added.
#' @export
#'
#' @examples
#' \donttest{
#' data(fish4_parameters)
#' sp <- fish4_parameters[["Oncorhynchus tshawytscha"]]$life_stages$adult
#' info <- fish4_parameters[["Oncorhynchus tshawytscha"]]$species_info
#' bio <- Bioenergetic(
#' species_params = sp,
#' species_info = info,
#' environmental_data = list(
#' temperature = data.frame(Day = 1:30, Temperature = rep(12, 30))
#' ),
#' diet_data = list(
#' proportions = data.frame(Day = 1:30, Prey1 = 1.0),
#' energies = data.frame(Day = 1:30, Prey1 = 5000),
#' prey_names = "Prey1"
#' ),
#' simulation_settings = list(initial_weight = 100, duration = 30)
#' )
#' bio$species_params$predator$ED_ini <- 5000
#' bio$species_params$predator$ED_end <- 5500
#' result <- run_fb4(bio, strategy = "direct", p_value = 0.5, verbose = FALSE)
#' growth <- analyze_growth_patterns(result)
#' }
analyze_growth_patterns <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
result_type <- detect_result_type(result)
method <- result_type$method
has_uncertainty <- result_type$has_uncertainty
# Get basic growth information
growth_info <- list(
method = method,
has_uncertainty = has_uncertainty,
individual_id = individual_id
)
# Extract growth metrics based on method
if (method == "hierarchical") {
if (is.null(individual_id)) {
# Population mean
pop_results <- get_population_results(result, confidence_level)
growth_info$initial_weight <- result$summary$initial_weight %||% NA
growth_info$final_weight <- list(
estimate = pop_results$mean_final_weight_est %||% NA,
se = pop_results$mean_final_weight_se %||% NA,
ci_lower = pop_results$mean_final_weight_ci_lower %||% NA,
ci_upper = pop_results$mean_final_weight_ci_upper %||% NA
)
growth_info$total_growth <- list(
estimate = pop_results$mean_total_growth_est %||% NA,
se = pop_results$mean_total_growth_se %||% NA,
ci_lower = pop_results$mean_total_growth_ci_lower %||% NA,
ci_upper = pop_results$mean_total_growth_ci_upper %||% NA
)
growth_info$relative_growth <- list(
estimate = pop_results$mean_relative_growth_est %||% NA,
se = pop_results$mean_relative_growth_se %||% NA,
ci_lower = pop_results$mean_relative_growth_ci_lower %||% NA,
ci_upper = pop_results$mean_relative_growth_ci_upper %||% NA
)
growth_info$n_individuals <- pop_results$n_individuals
} else {
# Individual result
ind_results <- get_individual_results(result, confidence_level)
if (individual_id > nrow(ind_results)) {
stop("Individual ID ", individual_id, " not found. Available: 1-", nrow(ind_results))
}
ind_data <- ind_results[individual_id, ]
growth_info$initial_weight <- result$summary$initial_weight %||% NA # Same for all individuals
growth_info$final_weight <- list(
estimate = ind_data$final_weight_est %||% NA,
se = ind_data$final_weight_se %||% NA,
ci_lower = ind_data$final_weight_ci_lower %||% NA,
ci_upper = ind_data$final_weight_ci_upper %||% NA
)
growth_info$total_growth <- list(
estimate = ind_data$total_growth_est %||% NA,
se = ind_data$total_growth_se %||% NA,
ci_lower = ind_data$total_growth_ci_lower %||% NA,
ci_upper = ind_data$total_growth_ci_upper %||% NA
)
growth_info$relative_growth <- list(
estimate = ind_data$relative_growth_est %||% NA,
se = ind_data$relative_growth_se %||% NA,
ci_lower = ind_data$relative_growth_ci_lower %||% NA,
ci_upper = ind_data$relative_growth_ci_upper %||% NA
)
growth_info$p_value <- list(
estimate = ind_data$p_estimate %||% NA,
se = ind_data$p_se %||% NA
)
}
} else {
# Non-hierarchical methods
growth_info$initial_weight <- result$summary$initial_weight %||% NA
if (method == "mle" && result$model_info$backend == "tmb") {
# MLE with TMB - has uncertainty
tmb_unc <- result$method_data$tmb_uncertainty
growth_info$final_weight <- list(
estimate = tmb_unc$final_weight_est %||% result$summary$predicted_weight %||% NA,
se = tmb_unc$final_weight_se %||% NA,
ci_lower = NA,
ci_upper = NA
)
growth_info$total_growth <- list(
estimate = tmb_unc$total_growth_est %||% NA,
se = tmb_unc$total_growth_se %||% NA,
ci_lower = NA,
ci_upper = NA
)
growth_info$relative_growth <- list(
estimate = tmb_unc$relative_growth_est %||% NA,
se = tmb_unc$relative_growth_se %||% NA,
ci_lower = NA,
ci_upper = NA
)
# Calculate CIs if we have estimates and SEs
z <- z_score(confidence_level)
for (metric in c("final_weight", "total_growth", "relative_growth")) {
est <- growth_info[[metric]]$estimate
se <- growth_info[[metric]]$se
if (!is.na(est) && !is.na(se)) {
growth_info[[metric]]$ci_lower <- est - z * se
growth_info[[metric]]$ci_upper <- est + z * se
}
}
} else {
# Traditional methods or MLE without full uncertainty
final_weight <- result$summary$final_weight %||% result$summary$predicted_weight %||% NA
growth_info$final_weight <- list(
estimate = final_weight,
se = NA,
ci_lower = NA,
ci_upper = NA
)
if (!is.na(final_weight) && !is.na(growth_info$initial_weight)) {
total_growth <- final_weight - growth_info$initial_weight
relative_growth <- (final_weight / growth_info$initial_weight - 1) * 100
growth_info$total_growth <- list(
estimate = total_growth,
se = NA,
ci_lower = NA,
ci_upper = NA
)
growth_info$relative_growth <- list(
estimate = relative_growth,
se = NA,
ci_lower = NA,
ci_upper = NA
)
}
}
# Add p_value information for fitted methods
if (method %in% c("mle", "binary_search", "optim")) {
growth_info$p_value <- list(
estimate = result$summary$p_estimate %||% result$summary$p_value %||% NA,
se = result$method_data$sigma_se %||% NA
)
}
}
# Calculate derived metrics
duration <- result$summary$simulation_days %||% NA
if (!is.na(duration) && !is.na(growth_info$total_growth$estimate)) {
# Daily growth rate (absolute)
growth_info$daily_growth_rate <- list(
estimate = growth_info$total_growth$estimate / duration,
se = if (!is.na(growth_info$total_growth$se)) growth_info$total_growth$se / duration else NA,
ci_lower = if (!is.na(growth_info$total_growth$ci_lower)) growth_info$total_growth$ci_lower / duration else NA,
ci_upper = if (!is.na(growth_info$total_growth$ci_upper)) growth_info$total_growth$ci_upper / duration else NA
)
# Specific growth rate (% per day)
if (!is.na(growth_info$initial_weight) && !is.na(growth_info$final_weight$estimate)) {
sgr <- log(growth_info$final_weight$estimate / growth_info$initial_weight) / duration * 100
growth_info$specific_growth_rate <- list(
estimate = sgr,
se = NA, # Would need delta method for proper SE
ci_lower = NA,
ci_upper = NA
)
}
}
return(growth_info)
}
# ============================================================================
# ENERGY BUDGET ANALYSIS FUNCTIONS
# ============================================================================
#' Analyze energy budget from FB4 results
#'
#' @description
#' Analyzes energy budget components from FB4 simulation results.
#' Calculates proportional allocation to different processes with
#' uncertainty propagation when available.
#'
#' @param result FB4 result object
#' @param individual_id Individual ID for hierarchical models (NULL for population/single individual)
#' @param confidence_level Confidence level for intervals (default 0.95)
#' @return A named list with four elements:
#' \describe{
#' \item{energy_components}{The list returned by
#' \code{\link{get_energy_budget_uncertainty}}, containing six component
#' sub-lists each with \code{estimate}, \code{se}, \code{ci_lower}, and
#' \code{ci_upper}.}
#' \item{proportions}{Named list of proportional allocations
#' (\code{prop_respiration}, \code{prop_egestion}, \code{prop_excretion},
#' \code{prop_sda}, \code{prop_net}), each a sub-list with \code{estimate},
#' \code{se}, \code{ci_lower}, and \code{ci_upper}. \code{NULL} when
#' consumption energy is zero or unavailable.}
#' \item{summary_metrics}{Named list with \code{gross_growth_efficiency},
#' \code{metabolic_scope}, and \code{assimilation_efficiency} sub-lists
#' (each \code{estimate} + \code{se}). \code{NULL} when proportions are
#' unavailable.}
#' \item{balance_check}{Named list with \code{consumption_energy},
#' \code{total_allocated}, \code{balance_error}, and
#' \code{relative_error} (all numeric) to verify mass-balance closure.}
#' }
#' Plus the context scalars \code{method}, \code{has_uncertainty}, and
#' \code{individual_id}.
#' @export
#' @examples
#' \donttest{
#' data(fish4_parameters)
#' sp <- fish4_parameters[["Oncorhynchus tshawytscha"]]$life_stages$adult
#' info <- fish4_parameters[["Oncorhynchus tshawytscha"]]$species_info
#' bio <- Bioenergetic(
#' species_params = sp,
#' species_info = info,
#' environmental_data = list(
#' temperature = data.frame(Day = 1:30, Temperature = rep(12, 30))
#' ),
#' diet_data = list(
#' proportions = data.frame(Day = 1:30, Prey1 = 1.0),
#' energies = data.frame(Day = 1:30, Prey1 = 5000),
#' prey_names = "Prey1"
#' ),
#' simulation_settings = list(initial_weight = 100, duration = 30)
#' )
#' bio$species_params$predator$ED_ini <- 5000
#' bio$species_params$predator$ED_end <- 5500
#' result <- run_fb4(bio, strategy = "direct", p_value = 0.5, verbose = FALSE)
#' budget <- analyze_energy_budget(result)
#' }
analyze_energy_budget <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
result_type <- detect_result_type(result)
method <- result_type$method
has_uncertainty <- result_type$has_uncertainty
# Get energy budget components
budget <- get_energy_budget_uncertainty(result, individual_id, confidence_level)
# Initialize analysis result
budget_analysis <- list(
method = method,
has_uncertainty = has_uncertainty,
individual_id = individual_id,
energy_components = budget
)
# Calculate energy proportions if consumption energy is available
consumption_est <- budget$consumption_energy$estimate
if (!is.na(consumption_est) && consumption_est > 0) {
# Calculate proportions
components <- c("respiration_energy", "egestion_energy", "excretion_energy", "sda_energy", "net_energy")
budget_analysis$proportions <- list()
for (component in components) {
comp_est <- budget[[component]]$estimate
comp_se <- budget[[component]]$se
if (!is.na(comp_est)) {
prop_est <- comp_est / consumption_est
# Approximate SE for proportion using delta method
prop_se <- NA
if (!is.na(comp_se) && !is.na(budget$consumption_energy$se)) {
# Delta method for ratio: Var(Y/X) ~ (Y/X)^2 * [Var(Y)/Y^2 + Var(X)/X^2 - 2*Cov(X,Y)/(X*Y)]
# Assuming independence: Cov(X,Y) = 0
cv_comp <- comp_se / comp_est
cv_cons <- budget$consumption_energy$se / consumption_est
prop_se <- prop_est * sqrt(cv_comp^2 + cv_cons^2)
}
prop_name <- gsub("_energy", "", component)
budget_analysis$proportions[[paste0("prop_", prop_name)]] <- list(
estimate = prop_est,
se = prop_se,
ci_lower = if (!is.na(prop_se)) prop_est - z_score(confidence_level) * prop_se else NA,
ci_upper = if (!is.na(prop_se)) prop_est + z_score(confidence_level) * prop_se else NA
)
}
}
# Calculate summary metrics
prop_respiration <- budget_analysis$proportions$prop_respiration$estimate %||% 0
prop_sda <- budget_analysis$proportions$prop_sda$estimate %||% 0
prop_net <- budget_analysis$proportions$prop_net$estimate %||% 0
budget_analysis$summary_metrics <- list(
gross_growth_efficiency = list(
estimate = prop_net,
se = budget_analysis$proportions$prop_net$se %||% NA
),
metabolic_scope = list(
estimate = prop_respiration + prop_sda,
se = NA # Would need covariance for proper SE
),
assimilation_efficiency = list(
estimate = 1 - (budget_analysis$proportions$prop_egestion$estimate %||% 0),
se = budget_analysis$proportions$prop_egestion$se %||% NA
)
)
# Energy balance check
total_allocated <- sum(vapply(components, function(x) budget[[x]]$estimate %||% 0, numeric(1)))
budget_analysis$balance_check <- list(
consumption_energy = consumption_est,
total_allocated = total_allocated,
balance_error = abs(total_allocated - consumption_est),
relative_error = abs(total_allocated - consumption_est) / consumption_est * 100
)
} else {
budget_analysis$proportions <- NULL
budget_analysis$summary_metrics <- NULL
budget_analysis$balance_check <- list(
consumption_energy = consumption_est,
error = "Consumption energy not available or zero"
)
}
return(budget_analysis)
}
# ============================================================================
# FEEDING ANALYSIS FUNCTIONS
# ============================================================================
#' Analyze feeding performance from FB4 results
#'
#' @description
#' Analyzes feeding-related metrics including consumption rates,
#' feeding efficiency, and p_value estimates with uncertainty.
#'
#' @param result FB4 result object
#' @param individual_id Individual ID for hierarchical models (NULL for population/single individual)
#' @param confidence_level Confidence level for intervals (default 0.95)
#' @return A named list with at minimum \code{method} (character),
#' \code{has_uncertainty} (logical), and \code{individual_id}. Additional
#' elements present when the relevant data are available:
#' \describe{
#' \item{total_consumption}{The list returned by
#' \code{\link{get_consumption_uncertainty}} (\code{estimate}, \code{se},
#' \code{ci_lower}, \code{ci_upper}, plus context scalars).}
#' \item{daily_consumption}{Sub-list (\code{estimate}, \code{se},
#' \code{ci_lower}, \code{ci_upper}) for the daily consumption rate
#' (g/day).}
#' \item{specific_consumption}{Sub-list (same four slots) for the
#' specific consumption rate (g consumption / g fish / day).}
#' \item{p_value}{Structure depends on method: for hierarchical it contains
#' \code{population_mean}, \code{population_se}, \code{population_sd}, and
#' \code{n_individuals}; for single-individual methods it contains
#' \code{estimate}, \code{se}, \code{ci_lower}, and \code{ci_upper}.}
#' \item{feeding_efficiency}{Sub-list (\code{estimate}, \code{se},
#' \code{ci_lower}, \code{ci_upper}) for the ratio of total growth to
#' total consumption (dimensionless).}
#' }
#' @export
#' @examples
#' \donttest{
#' data(fish4_parameters)
#' sp <- fish4_parameters[["Oncorhynchus tshawytscha"]]$life_stages$adult
#' info <- fish4_parameters[["Oncorhynchus tshawytscha"]]$species_info
#' bio <- Bioenergetic(
#' species_params = sp,
#' species_info = info,
#' environmental_data = list(
#' temperature = data.frame(Day = 1:30, Temperature = rep(12, 30))
#' ),
#' diet_data = list(
#' proportions = data.frame(Day = 1:30, Prey1 = 1.0),
#' energies = data.frame(Day = 1:30, Prey1 = 5000),
#' prey_names = "Prey1"
#' ),
#' simulation_settings = list(initial_weight = 100, duration = 30)
#' )
#' bio$species_params$predator$ED_ini <- 5000
#' bio$species_params$predator$ED_end <- 5500
#' result <- run_fb4(bio, strategy = "direct", p_value = 0.5, verbose = FALSE)
#' feeding <- analyze_feeding_performance(result)
#' }
analyze_feeding_performance <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
result_type <- detect_result_type(result)
method <- result_type$method
# Get consumption data
consumption <- get_consumption_uncertainty(result, individual_id, confidence_level)
# Initialize feeding analysis
feeding_analysis <- list(
method = method,
has_uncertainty = consumption$has_uncertainty,
individual_id = individual_id
)
# Basic consumption metrics
feeding_analysis$total_consumption <- consumption
# Calculate consumption rates
z <- z_score(confidence_level)
initial_weight <- result$summary$initial_weight %||% NA
simulation_days <- result$summary$simulation_days %||% NA
if (!is.na(consumption$estimate) && !is.na(initial_weight) && !is.na(simulation_days)) {
# Daily consumption rate (g/day)
daily_consumption <- consumption$estimate / simulation_days
daily_consumption_se <- if (!is.na(consumption$se)) consumption$se / simulation_days else NA
feeding_analysis$daily_consumption <- list(
estimate = daily_consumption,
se = daily_consumption_se,
ci_lower = if (!is.na(daily_consumption_se)) daily_consumption - z * daily_consumption_se else NA,
ci_upper = if (!is.na(daily_consumption_se)) daily_consumption + z * daily_consumption_se else NA
)
# Specific consumption rate (g consumption / g fish / day)
specific_consumption <- daily_consumption / initial_weight
specific_consumption_se <- if (!is.na(daily_consumption_se)) daily_consumption_se / initial_weight else NA
feeding_analysis$specific_consumption <- list(
estimate = specific_consumption,
se = specific_consumption_se,
ci_lower = if (!is.na(specific_consumption_se)) specific_consumption - z * specific_consumption_se else NA,
ci_upper = if (!is.na(specific_consumption_se)) specific_consumption + z * specific_consumption_se else NA
)
}
# p_value analysis (feeding rate)
if (method == "hierarchical") {
if (is.null(individual_id)) {
# Population parameters
pop_results <- get_population_results(result, confidence_level)
feeding_analysis$p_value <- list(
population_mean = pop_results$mu_p_estimate %||% NA,
population_se = pop_results$mu_p_se %||% NA,
population_sd = pop_results$sigma_p_estimate %||% NA,
n_individuals = pop_results$n_individuals
)
} else {
# Individual p_value
ind_results <- get_individual_results(result, confidence_level)
if (individual_id <= nrow(ind_results)) {
feeding_analysis$p_value <- list(
estimate = ind_results$p_estimate[individual_id] %||% NA,
se = ind_results$p_se[individual_id] %||% NA
)
}
}
} else if (method %in% c("mle", "binary_search", "optim")) {
# Single p_value estimate
feeding_analysis$p_value <- list(
estimate = result$summary$p_estimate %||% result$summary$p_value %||% NA,
se = result$method_data$sigma_se %||% NA,
ci_lower = result$method_data$confidence_intervals$p_ci_lower %||% NA,
ci_upper = result$method_data$confidence_intervals$p_ci_upper %||% NA
)
}
# Feeding efficiency (if growth data available)
growth <- analyze_growth_patterns(result, individual_id, confidence_level)
if (!is.na(growth$total_growth$estimate) && !is.na(consumption$estimate)) {
feeding_efficiency <- growth$total_growth$estimate / consumption$estimate
# Approximate SE using delta method
feeding_efficiency_se <- NA
if (!is.na(growth$total_growth$se) && !is.na(consumption$se)) {
cv_growth <- growth$total_growth$se / growth$total_growth$estimate
cv_consumption <- consumption$se / consumption$estimate
feeding_efficiency_se <- feeding_efficiency * sqrt(cv_growth^2 + cv_consumption^2)
}
feeding_analysis$feeding_efficiency <- list(
estimate = feeding_efficiency,
se = feeding_efficiency_se,
ci_lower = if (!is.na(feeding_efficiency_se)) feeding_efficiency - z * feeding_efficiency_se else NA,
ci_upper = if (!is.na(feeding_efficiency_se)) feeding_efficiency + z * feeding_efficiency_se else NA
)
}
return(feeding_analysis)
}
# ============================================================================
# SUMMARY STATISTICS FUNCTIONS
# ============================================================================
#' Comprehensive post-simulation analysis summary
#'
#' @description
#' **Post-hoc analysis function** — takes a finished \code{fb4_result} object
#' and bundles growth, feeding, and energy-budget analyses into a single list.
#' Useful when you need all major metrics in one call.
#'
#' This is different from the internal \code{$summary} slot (built automatically
#' during result construction by \code{create_unified_summary()}). This function
#' re-derives richer metrics from \code{daily_output} and supports uncertainty
#' propagation for MLE / bootstrap / hierarchical results.
#'
#' @param result An \code{fb4_result} object returned by \code{run_fb4()}
#' @param individual_id For hierarchical models: individual ID to extract
#' (\code{NULL} returns population-level summary)
#' @param confidence_level Confidence level for uncertainty intervals, default 0.95
#' @return Named list with \code{model_info}, \code{growth}, \code{feeding},
#' \code{energy_budget}, and \code{model_fit} sections
#' @seealso \code{\link{analyze_growth_patterns}}, \code{\link{analyze_feeding_performance}},
#' \code{\link{analyze_energy_budget}}
#' @export
create_result_summary <- function(result, individual_id = NULL, confidence_level = 0.95) {
if (!is.fb4_result(result)) {
stop("Input must be an fb4_result object")
}
# Get all major analyses
growth <- analyze_growth_patterns(result, individual_id, confidence_level)
feeding <- analyze_feeding_performance(result, individual_id, confidence_level)
budget <- analyze_energy_budget(result, individual_id, confidence_level)
summary_result <- list(
# Model information
model_info = list(
method = result$summary$method,
backend = result$model_info$backend,
has_uncertainty = growth$has_uncertainty,
individual_id = individual_id,
confidence_level = confidence_level
),
# Growth metrics
growth = list(
initial_weight = growth$initial_weight,
final_weight = growth$final_weight,
total_growth = growth$total_growth,
relative_growth = growth$relative_growth,
daily_growth_rate = growth$daily_growth_rate,
specific_growth_rate = growth$specific_growth_rate
),
# Feeding metrics
feeding = list(
total_consumption = feeding$total_consumption,
daily_consumption = feeding$daily_consumption,
specific_consumption = feeding$specific_consumption,
p_value = feeding$p_value,
feeding_efficiency = feeding$feeding_efficiency
),
# Energy budget
energy_budget = list(
components = budget$energy_components,
proportions = budget$proportions,
summary_metrics = budget$summary_metrics,
balance_check = budget$balance_check
),
# Model fit (for statistical methods)
model_fit = if (result$summary$method %in% c("mle", "hierarchical")) {
list(
converged = result$summary$converged,
log_likelihood = result$method_data$model_fit$log_likelihood %||% result$method_data$log_likelihood,
aic = result$method_data$model_fit$aic %||% result$method_data$aic,
bic = result$method_data$model_fit$bic %||% result$method_data$bic
)
} else {
list(
converged = result$summary$converged %||% TRUE
)
}
)
return(summary_result)
}
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