Análises de predição - execução e saídas

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Last updated: 2026-04-08

Checks: 7 0

Knit directory: Integrating-nir-genomic-kernel/

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R Markdown file: up-to-date

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Environment: empty

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Seed: set.seed(20250829)

The command set.seed(20250829) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: none

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File paths: relative

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Repository version: 88e9ac3

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The results in this page were generated with repository version 88e9ac3. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

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---

Language / Idioma: English | Português

Navegação do módulo / Module navigation: Parte I: setup, modelos e desenho de CV

1. Introdução

Este submódulo é a contraparte de execução de analysis_prediction_pt.Rmd. Seu papel é rodar os ajustes de predição para o catálogo reduzido de 18 modelos, salvar os arquivos CSV em output/results/ e inventariar as saídas que mais adiante serão consumidas pela etapa de visualização.

O contrato de execução usado aqui segue quatro regras operacionais:

• CV1 e CV2 são derivados do mesmo ajuste em cada fold;
• CV0 e CV00 são derivados do mesmo ajuste para cada ambiente deixado de fora em cada fold;
• a partição em folds é determinística para cada tarefa MODEL × REP × TRAIT e é reutilizada nos dois blocos de CV;
• os arquivos de saída são organizados explicitamente por esquema de validação, ambiente quando aplicável, trait, repetição e fold, com o fold representado tanto na árvore de pastas quanto no nome do arquivo.

Os chunks pesados de ajuste permanecem desativados por padrão no tutorial. Ainda assim, o código mostrado abaixo é a versão executável que deve ser usada quando a etapa de predição for rerodada.

2. Pacotes e caminhos do projeto

O módulo de execução precisa tanto dos objetos matriciais criados a montante quanto dos pacotes usados pela camada de ajuste paralelo. A lógica de caminhos abaixo mantém o arquivo executável tanto a partir da raiz do projeto quanto do diretório analysis/.

rm(list = ls())
options(scipen = 999)

library(readr)

Warning: pacote 'readr' foi compilado no R versão 4.5.2

library(dplyr)

Warning: pacote 'dplyr' foi compilado no R versão 4.5.2

Anexando pacote: 'dplyr'

Os seguintes objetos são mascarados por 'package:stats':

    filter, lag

Os seguintes objetos são mascarados por 'package:base':

    intersect, setdiff, setequal, union

library(stringr)
library(knitr)
library(BGLR)
library(foreach)

Warning: pacote 'foreach' foi compilado no R versão 4.5.2

library(doParallel)

Warning: pacote 'doParallel' foi compilado no R versão 4.5.2

Carregando pacotes exigidos: iterators

Carregando pacotes exigidos: parallel

library(parallel)
library(tidyr)

Warning: pacote 'tidyr' foi compilado no R versão 4.5.2

project_root <- getwd()

if (!dir.exists(file.path(project_root, "output")) &&
    dir.exists(file.path(project_root, "..", "output"))) {
  project_root <- normalizePath(file.path(project_root, ".."))
}

if (!dir.exists(file.path(project_root, "output"))) {
  stop("Não foi possível encontrar a pasta 'output/'. Renderize este arquivo a partir da raiz do projeto ou de analysis/.")
}

results_dir <- file.path(project_root, "output", "results")
analysis_tables_dir <- file.path(project_root, "output", "tables", "analysis_prediction")
bglr_runs_dir <- file.path(results_dir, "bglr_runs")

dir.create(results_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(analysis_tables_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(bglr_runs_dir, recursive = TRUE, showWarnings = FALSE)

3. Recarregar os objetos de execução da Parte I

Esta seção reconstrói o ambiente de execução quando o arquivo é renderizado isoladamente. Ela segue o contrato de analysis_prediction_pt.Rmd e recria exatamente o conjunto reduzido de 18 objetos ETA definido na Parte I.

matrices_dir <- file.path(project_root, "output", "Matrizes")

required_matrix_files <- c(
  "Pheno.rds",
  "ZG.rds", "ZP.rds", "ZE.rds", "ZW.rds",
  "ZGZE.rds", "ZPZE.rds", "ZGZW.rds", "ZPZW.rds",
  "GGK.rds", "PGK.rds", "GAK.rds", "PAK.rds",
  "GGKE.rds", "PGKE.rds", "GAKE.rds", "PAKE.rds",
  "GGKW.rds", "PGKW.rds", "GAKW.rds", "PAKW.rds"
)

required_matrix_paths <- file.path(matrices_dir, required_matrix_files)

if (!all(file.exists(required_matrix_paths))) {
  missing_matrix_files <- required_matrix_files[!file.exists(required_matrix_paths)]
  stop(
    paste0(
      "Os objetos de execução exigidos por analysis_prediction_run_outputs não foram encontrados em output/Matrizes/. Arquivos ausentes: ",
      paste(missing_matrix_files, collapse = ", ")
    )
  )
}

Pheno <- readRDS(file.path(matrices_dir, "Pheno.rds"))
ZG   <- readRDS(file.path(matrices_dir, "ZG.rds"))
ZP   <- readRDS(file.path(matrices_dir, "ZP.rds"))
ZE   <- readRDS(file.path(matrices_dir, "ZE.rds"))
ZW   <- readRDS(file.path(matrices_dir, "ZW.rds"))
ZGZE <- readRDS(file.path(matrices_dir, "ZGZE.rds"))
ZPZE <- readRDS(file.path(matrices_dir, "ZPZE.rds"))
ZGZW <- readRDS(file.path(matrices_dir, "ZGZW.rds"))
ZPZW <- readRDS(file.path(matrices_dir, "ZPZW.rds"))
GGK  <- readRDS(file.path(matrices_dir, "GGK.rds"))
PGK  <- readRDS(file.path(matrices_dir, "PGK.rds"))
GAK  <- readRDS(file.path(matrices_dir, "GAK.rds"))
PAK  <- readRDS(file.path(matrices_dir, "PAK.rds"))
GGKE <- readRDS(file.path(matrices_dir, "GGKE.rds"))
PGKE <- readRDS(file.path(matrices_dir, "PGKE.rds"))
GAKE <- readRDS(file.path(matrices_dir, "GAKE.rds"))
PAKE <- readRDS(file.path(matrices_dir, "PAKE.rds"))
GGKW <- readRDS(file.path(matrices_dir, "GGKW.rds"))
PGKW <- readRDS(file.path(matrices_dir, "PGKW.rds"))
GAKW <- readRDS(file.path(matrices_dir, "GAKW.rds"))
PAKW <- readRDS(file.path(matrices_dir, "PAKW.rds"))

if (!exists("Pedigree")) Pedigree <- unique(Pheno$Pedigree)

candidate_traits <- c("GY", "KW")
traits_to_run <- candidate_traits[candidate_traits %in% names(Pheno)]
if (length(traits_to_run) == 0) {
  stop("Nenhum dos traits esperados (GY, KW) foi encontrado em Pheno.")
}

if (!exists("nIter"))   nIter   <- 5000
if (!exists("burnIn"))  burnIn  <- 1000
if (!exists("thin"))    thin    <- 10
if (!exists("num_rep")) num_rep <- 10
if (!exists("n_cores")) n_cores <- max(1L, parallel::detectCores() - 1L)

model_catalog <- data.frame(
  ModelNumber = 1:18,
  Model = paste0("Eta", 1:18),
  ShortDescription = c(
    "G + E",
    "P + E",
    "GGK + E",
    "PGK + E",
    "GAK + E",
    "PAK + E",
    "G+P + E",
    "GGK+PGK + E",
    "GAK+PAK + E",
    "G + W",
    "P + W",
    "G+P + W",
    "GGK + W",
    "PGK + W",
    "GGK+PGK + W",
    "GAK + W",
    "PAK + W",
    "GAK+PAK + W"
  ),
  Description = c(
    "E + G + GE",
    "E + P + PE",
    "E + GGK + GGKE",
    "E + PGK + PGKE",
    "E + GAK + GAKE",
    "E + PAK + PAKE",
    "E + G + P + GE + PE",
    "E + GGK + PGK + GGKE + PGKE",
    "E + GAK + PAK + GAKE + PAKE",
    "E + W + G + GE + GW",
    "E + W + P + PE + PW",
    "E + W + G + P + GE + PE + GW + PW",
    "E + W + GGK + GGKE + GGKW",
    "E + W + PGK + PGKE + PGKW",
    "E + W + GGK + PGK + GGKE + PGKE + GGKW + PGKW",
    "E + W + GAK + GAKE + GAKW",
    "E + W + PAK + PAKE + PAKW",
    "E + W + GAK + PAK + GAKE + PAKE + GAKW + PAKW"
  ),
  FullEffects = c(
    "E + G + GE",
    "E + P + PE",
    "E + GGK + GGKE",
    "E + PGK + PGKE",
    "E + GAK + GAKE",
    "E + PAK + PAKE",
    "E + G + P + GE + PE",
    "E + GGK + PGK + GGKE + PGKE",
    "E + GAK + PAK + GAKE + PAKE",
    "E + W + G + GE + GW",
    "E + W + P + PE + PW",
    "E + W + G + P + GE + PE + GW + PW",
    "E + W + GGK + GGKE + GGKW",
    "E + W + PGK + PGKE + PGKW",
    "E + W + GGK + PGK + GGKE + PGKE + GGKW + PGKW",
    "E + W + GAK + GAKE + GAKW",
    "E + W + PAK + PAKE + PAKW",
    "E + W + GAK + PAK + GAKE + PAKE + GAKW + PAKW"
  ),
  InterpretiveDescription = c(
    "Modelo genômico linear com ambiente e interação G × E",
    "Modelo fenômico linear com ambiente e interação P × E",
    "Modelo genômico Gaussiano com ambiente e interação GGK × E",
    "Modelo fenômico Gaussiano com ambiente e interação PGK × E",
    "Modelo genômico arc-cosine com ambiente e interação GAK × E",
    "Modelo fenômico arc-cosine com ambiente e interação PAK × E",
    "Modelo combinado linear com ambiente, GE e PE",
    "Modelo combinado Gaussiano com ambiente, GGKE e PGKE",
    "Modelo combinado arc-cosine com ambiente, GAKE e PAKE",
    "Modelo genômico linear com ambiente, clima, GE e GW",
    "Modelo fenômico linear com ambiente, clima, PE e PW",
    "Modelo combinado linear com ambiente, clima, GE, PE, GW e PW",
    "Modelo genômico Gaussiano com ambiente, clima, GGKE e GGKW",
    "Modelo fenômico Gaussiano com ambiente, clima, PGKE e PGKW",
    "Modelo combinado Gaussiano com ambiente, clima, GGKE, PGKE, GGKW e PGKW",
    "Modelo genômico arc-cosine com ambiente, clima, GAKE e GAKW",
    "Modelo fenômico arc-cosine com ambiente, clima, PAKE e PAKW",
    "Modelo combinado arc-cosine com ambiente, clima, GAKE, PAKE, GAKW e PAKW"
  ),
  stringsAsFactors = FALSE
)

env_design <- model.matrix(~ Pheno$Env - 1)

Eta_list <- vector("list", 18)
Eta_list[[1]]  <- list(list(X = env_design, model = "BRR"), list(K = ZG, model = "RKHS"), list(K = ZGZE, model = "RKHS"))
Eta_list[[2]]  <- list(list(X = env_design, model = "BRR"), list(K = ZP, model = "RKHS"), list(K = ZPZE, model = "RKHS"))
Eta_list[[3]]  <- list(list(X = env_design, model = "BRR"), list(K = GGK, model = "RKHS"), list(K = GGKE, model = "RKHS"))
Eta_list[[4]]  <- list(list(X = env_design, model = "BRR"), list(K = PGK, model = "RKHS"), list(K = PGKE, model = "RKHS"))
Eta_list[[5]]  <- list(list(X = env_design, model = "BRR"), list(K = GAK, model = "RKHS"), list(K = GAKE, model = "RKHS"))
Eta_list[[6]]  <- list(list(X = env_design, model = "BRR"), list(K = PAK, model = "RKHS"), list(K = PAKE, model = "RKHS"))
Eta_list[[7]]  <- list(list(X = env_design, model = "BRR"), list(K = ZG, model = "RKHS"), list(K = ZP, model = "RKHS"), list(K = ZGZE, model = "RKHS"), list(K = ZPZE, model = "RKHS"))
Eta_list[[8]]  <- list(list(X = env_design, model = "BRR"), list(K = GGK, model = "RKHS"), list(K = PGK, model = "RKHS"), list(K = GGKE, model = "RKHS"), list(K = PGKE, model = "RKHS"))
Eta_list[[9]]  <- list(list(X = env_design, model = "BRR"), list(K = GAK, model = "RKHS"), list(K = PAK, model = "RKHS"), list(K = GAKE, model = "RKHS"), list(K = PAKE, model = "RKHS"))
Eta_list[[10]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = ZG, model = "RKHS"), list(K = ZGZE, model = "RKHS"), list(K = ZGZW, model = "RKHS"))
Eta_list[[11]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = ZP, model = "RKHS"), list(K = ZPZE, model = "RKHS"), list(K = ZPZW, model = "RKHS"))
Eta_list[[12]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = ZG, model = "RKHS"), list(K = ZP, model = "RKHS"), list(K = ZGZE, model = "RKHS"), list(K = ZPZE, model = "RKHS"), list(K = ZGZW, model = "RKHS"), list(K = ZPZW, model = "RKHS"))
Eta_list[[13]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = GGK, model = "RKHS"), list(K = GGKE, model = "RKHS"), list(K = GGKW, model = "RKHS"))
Eta_list[[14]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = PGK, model = "RKHS"), list(K = PGKE, model = "RKHS"), list(K = PGKW, model = "RKHS"))
Eta_list[[15]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = GGK, model = "RKHS"), list(K = PGK, model = "RKHS"), list(K = GGKE, model = "RKHS"), list(K = PGKE, model = "RKHS"), list(K = GGKW, model = "RKHS"), list(K = PGKW, model = "RKHS"))
Eta_list[[16]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = GAK, model = "RKHS"), list(K = GAKE, model = "RKHS"), list(K = GAKW, model = "RKHS"))
Eta_list[[17]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = PAK, model = "RKHS"), list(K = PAKE, model = "RKHS"), list(K = PAKW, model = "RKHS"))
Eta_list[[18]] <- list(list(X = env_design, model = "BRR"), list(K = ZW, model = "RKHS"), list(K = GAK, model = "RKHS"), list(K = PAK, model = "RKHS"), list(K = GAKE, model = "RKHS"), list(K = PAKE, model = "RKHS"), list(K = GAKW, model = "RKHS"), list(K = PAKW, model = "RKHS"))

4. Definir o contrato de execução

Este bloco restaura os objetos operacionais usados pelo workflow atual: o conjunto fixo de ambientes leave-one-environment-out, o número de folds e a grade de tarefas MODEL × REP × TRAIT para o catálogo de 18 modelos. A lógica de ajuste e de resumo fica escrita diretamente dentro dos blocos de CV para manter o caminho de execução explícito.

envs_leaveout <- c("CS11_WS", "CS11_WW", "CS12_WS", "CS12_WW")
envs_leaveout <- envs_leaveout[envs_leaveout %in% unique(Pheno$Env)]

num_fold <- 5L
n_hybrids <- length(Pedigree)
combos <- expand.grid(
  MODEL = seq_along(Eta_list),
  REP = seq_len(num_rep),
  TRAIT = traits_to_run,
  stringsAsFactors = FALSE
)

5. Iniciar o backend paralelo

O backend abaixo espelha o fluxo original: um worker recebe uma combinação de modelo, repetição e trait.

useCores <- max(1L, n_cores)
cl <- parallel::makeCluster(useCores)
doParallel::registerDoParallel(cl)

parallel::clusterEvalQ(cl, {
  library(BGLR)
  library(dplyr)
  library(plyr)
  NULL
})

cat("Rodando em ", useCores, " núcleos.")

6. Executar as análises preditivas para CV1 e CV2

Este bloco implementa a validação baseada em genótipos para o workflow de 18 modelos. O mapa determinístico de folds, o ajuste do BGLR e o resumo por ambiente ficam escritos diretamente dentro do laço para manter cada etapa explícita. Um ajuste é rodado por fold e as predições resultantes são divididas em CV1 (genótipos deixados de fora) e CV2 (genótipos de treinamento sob o mesmo ajuste). No padrão atual, tanto os CSVs finais quanto os artefatos de ajuste do BGLR são salvos em uma hierarquia rep/fold.

foreach(i = seq_len(nrow(combos)),
        .packages = c("BGLR", "dplyr", "plyr")) %dopar% {
  MODEL <- combos$MODEL[i]
  rep_num <- combos$REP[i]
  TRAIT <- as.character(combos$TRAIT[i])

  pheno <- data.frame(Pheno)

  trait_id <- match(TRAIT, trait_reference_order)
  if (is.na(trait_id)) {
    trait_id <- 1L
  }

  seed_value <- as.integer(100000L * MODEL + 100L * rep_num + trait_id)
  old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
  if (old_seed_exists) {
    old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
  }

  set.seed(seed_value)
  obs_per_fold <- ceiling(length(Pedigree) / num_fold)
  folds <- sample(rep(seq_len(num_fold), each = obs_per_fold, length.out = length(Pedigree)))

  if (old_seed_exists) {
    assign(".Random.seed", old_seed, envir = .GlobalEnv)
  } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) {
    rm(".Random.seed", envir = .GlobalEnv)
  }

  folds_df <- data.frame(Pedigree = Pedigree, fold = folds, stringsAsFactors = FALSE)

  for (fold_num in seq_len(num_fold)) {
    rep_label <- sprintf("rep%02d", rep_num)
    fold_label <- sprintf("fold%02d", fold_num)

    cv1_dir <- file.path(results_dir, "CV1", TRAIT, rep_label, fold_label)
    cv2_dir <- file.path(results_dir, "CV2", TRAIT, rep_label, fold_label)
    dir.create(cv1_dir, recursive = TRUE, showWarnings = FALSE)
    dir.create(cv2_dir, recursive = TRUE, showWarnings = FALSE)

    cv1_file <- file.path(cv1_dir, paste0("CV1_", TRAIT, "_Eta", MODEL, "_", rep_label, "_", fold_label, ".csv"))
    cv2_file <- file.path(cv2_dir, paste0("CV2_", TRAIT, "_Eta", MODEL, "_", rep_label, "_", fold_label, ".csv"))

    if (file.exists(cv1_file) && file.exists(cv2_file)) {
      next
    }

    train_geno <- folds_df$Pedigree[folds_df$fold != fold_num]

    yield <- pheno
    yield$Y2 <- NA
    yield$Y2[yield$Pedigree %in% train_geno] <- yield[[TRAIT]][yield$Pedigree %in% train_geno]

    run_dir <- file.path(bglr_runs_dir, "CV1", TRAIT, rep_label, fold_label, paste0("Eta", MODEL))
    dir.create(run_dir, recursive = TRUE, showWarnings = FALSE)

    save_prefix_full <- file.path(run_dir, paste0("CV1_", TRAIT, "_Eta", MODEL, "_", rep_label, "_", fold_label, "_"))

    y_t <- as.numeric(yield$Y2)
    start_time <- Sys.time()
    fit <- BGLR(y = y_t, ETA = Eta_list[[MODEL]], nIter = nIter, burnIn = burnIn, thin = thin, saveAt = save_prefix_full)
    end_time <- Sys.time()
    runtime_full <- as.numeric(difftime(end_time, start_time, units = "secs"))

    yield$yhat <- fit$yHat
    rm(fit)
    gc()

    df_test <- yield[!yield$Pedigree %in% train_geno, ]
    df_train <- yield[yield$Pedigree %in% train_geno, ]

    tmp1 <- df_test %>%
      dplyr::group_by(Env) %>%
      dplyr::summarize(cor = cor(.data[[TRAIT]], yhat, use = "complete.obs"), .groups = "drop") %>%
      as.data.frame()

    tmp2 <- df_train %>%
      dplyr::group_by(Env) %>%
      dplyr::summarize(cor = cor(.data[[TRAIT]], yhat, use = "complete.obs"), .groups = "drop") %>%
      as.data.frame()

    tmp1$Runtime_sec <- runtime_full
    tmp2$Runtime_sec <- runtime_full
    tmp1$Fold <- fold_label
    tmp2$Fold <- fold_label

    write.csv(tmp1, file = cv1_file, row.names = FALSE)
    write.csv(tmp2, file = cv2_file, row.names = FALSE)
  }

  NULL
}

7. Executar as análises preditivas para CV0 e CV00

Este bloco implementa a validação leave-one-environment-out para o mesmo workflow de 18 modelos. Para cada tarefa, o mapa determinístico de folds é reconstruído com a mesma regra usada em CV1/CV2, um ajuste é rodado para cada combinação env_leave × fold e as predições são divididas em CV0 (genótipos de treinamento sob a máscara do ambiente) e CV00 (genótipos deixados de fora sob a mesma máscara). Assim como no bloco baseado em genótipos, as saídas são gravadas em uma hierarquia rep/fold e os artefatos do BGLR são preservados em pastas persistentes correspondentes.

foreach(i = seq_len(nrow(combos)),
        .packages = c("BGLR", "dplyr", "plyr")) %dopar% {
  MODEL <- combos$MODEL[i]
  rep_num <- combos$REP[i]
  TRAIT <- as.character(combos$TRAIT[i])

  pheno <- data.frame(Pheno)

  trait_id <- match(TRAIT, trait_reference_order)
  if (is.na(trait_id)) {
    trait_id <- 1L
  }

  seed_value <- as.integer(100000L * MODEL + 100L * rep_num + trait_id)
  old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
  if (old_seed_exists) {
    old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE)
  }

  set.seed(seed_value)
  obs_per_fold <- ceiling(length(Pedigree) / num_fold)
  folds <- sample(rep(seq_len(num_fold), each = obs_per_fold, length.out = length(Pedigree)))

  if (old_seed_exists) {
    assign(".Random.seed", old_seed, envir = .GlobalEnv)
  } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) {
    rm(".Random.seed", envir = .GlobalEnv)
  }

  folds_df <- data.frame(Pedigree = Pedigree, fold = folds, stringsAsFactors = FALSE)

  for (fold_num in seq_len(num_fold)) {
    train_geno <- folds_df$Pedigree[folds_df$fold != fold_num]
    rep_label <- sprintf("rep%02d", rep_num)
    fold_label <- sprintf("fold%02d", fold_num)

    for (env_idx in seq_along(envs_leaveout)) {
      env_name <- envs_leaveout[env_idx]

      cv00_dir <- file.path(results_dir, "CV00", env_name, TRAIT, rep_label, fold_label)
      cv0_dir <- file.path(results_dir, "CV0", env_name, TRAIT, rep_label, fold_label)
      dir.create(cv00_dir, recursive = TRUE, showWarnings = FALSE)
      dir.create(cv0_dir, recursive = TRUE, showWarnings = FALSE)

      cv00_file <- file.path(cv00_dir, paste0("CV00_", TRAIT, "_", env_name, "_Eta", MODEL, "_", rep_label, "_", fold_label, ".csv"))
      cv0_file <- file.path(cv0_dir, paste0("CV0_", TRAIT, "_", env_name, "_Eta", MODEL, "_", rep_label, "_", fold_label, ".csv"))

      if (file.exists(cv00_file) && file.exists(cv0_file)) {
        next
      }

      yield2 <- pheno
      yield2$Y2 <- NA
      yield2$Y2[yield2$Pedigree %in% train_geno] <- yield2[[TRAIT]][yield2$Pedigree %in% train_geno]
      yield2$Y2[yield2$Env == env_name] <- NA

      run_dir <- file.path(bglr_runs_dir, "CV0", env_name, TRAIT, rep_label, fold_label, paste0("Eta", MODEL))
      dir.create(run_dir, recursive = TRUE, showWarnings = FALSE)

      save_prefix_env <- file.path(run_dir, paste0("CV0_", env_name, "_", TRAIT, "_Eta", MODEL, "_", rep_label, "_", fold_label, "_"))

      y_t <- as.numeric(yield2$Y2)
      start_time <- Sys.time()
      fit <- BGLR(y = y_t, ETA = Eta_list[[MODEL]], nIter = nIter, burnIn = burnIn, thin = thin, saveAt = save_prefix_env)
      end_time <- Sys.time()
      runtime_env <- as.numeric(difftime(end_time, start_time, units = "secs"))

      yield2$yhat <- fit$yHat
      rm(fit)
      gc()

      df_test2 <- yield2[!yield2$Pedigree %in% train_geno & yield2$Env == env_name, , drop = FALSE]
      df_train2 <- yield2[yield2$Pedigree %in% train_geno & yield2$Env == env_name, , drop = FALSE]

      tmp00 <- df_test2 %>%
        dplyr::group_by(Env) %>%
        dplyr::summarize(cor = cor(.data[[TRAIT]], yhat, use = "complete.obs"), .groups = "drop") %>%
        as.data.frame()

      tmp0 <- df_train2 %>%
        dplyr::group_by(Env) %>%
        dplyr::summarize(cor = cor(.data[[TRAIT]], yhat, use = "complete.obs"), .groups = "drop") %>%
        as.data.frame()

      tmp00$Runtime_sec <- runtime_env
      tmp0$Runtime_sec <- runtime_env
      tmp00$Fold <- fold_label
      tmp0$Fold <- fold_label

      write.csv(tmp00, file = cv00_file, row.names = FALSE)
      write.csv(tmp0, file = cv0_file, row.names = FALSE)
    }
  }

  NULL
}

8. Encerrar o backend paralelo

stopCluster(cl)
foreach::registerDoSEQ()

9. Inventário dos arquivos de saída de predição

Os inventários abaixo resumem os arquivos CSV produzidos pela etapa de execução usando o contrato atual de nomenclatura com 18 modelos adotado nesta versão.

prediction_files <- character(0)

if (dir.exists(results_dir)) {
  prediction_files <- list.files(results_dir, pattern = "\\.csv$", full.names = TRUE, recursive = TRUE)
}

inventory_cv12 <- data.frame()
inventory_cvloo <- data.frame()

if (length(prediction_files) > 0) {
  results_root <- normalizePath(results_dir, winslash = "/")

  for (current_file in prediction_files) {
    current_norm <- normalizePath(current_file, winslash = "/")
    rel_path <- substring(current_norm, nchar(results_root) + 2)
    path_parts <- strsplit(rel_path, "/", fixed = TRUE)[[1]]
    current_name <- basename(current_file)
    current_name_no_ext <- sub("\\.csv$", "", current_name)
    current_parts <- strsplit(current_name_no_ext, "_")[[1]]

    if (length(path_parts) >= 5 && current_parts[1] %in% c("CV1", "CV2")) {
      inventory_cv12 <- dplyr::bind_rows(
        inventory_cv12,
        data.frame(
          relative_path = rel_path,
          file_name = current_name,
          CV = current_parts[1],
          Trait = path_parts[2],
          Rep = path_parts[3],
          Fold = path_parts[4],
          Model = current_parts[3],
          stringsAsFactors = FALSE
        )
      )
    }

    if (length(path_parts) >= 6 && current_parts[1] %in% c("CV0", "CV00")) {
      inventory_cvloo <- dplyr::bind_rows(
        inventory_cvloo,
        data.frame(
          relative_path = rel_path,
          file_name = current_name,
          CV = current_parts[1],
          Env_leave = path_parts[2],
          Trait = path_parts[3],
          Rep = path_parts[4],
          Fold = path_parts[5],
          Model = current_parts[length(current_parts) - 2],
          stringsAsFactors = FALSE
        )
      )
    }
  }
}

write_csv(inventory_cv12, file.path(analysis_tables_dir, "analysis_prediction_inventory_cv12.csv"))
write_csv(inventory_cvloo, file.path(analysis_tables_dir, "analysis_prediction_inventory_cvloo.csv"))

10. Contagens de arquivos por esquema de validação e trait

counts_by_cv <- data.frame()
counts_by_trait <- data.frame()

if (nrow(inventory_cv12) > 0 || nrow(inventory_cvloo) > 0) {
  combined_inventory <- bind_rows(
    inventory_cv12 %>% mutate(Env_leave = NA_character_),
    inventory_cvloo
  )

  counts_by_cv <- combined_inventory %>%
    count(CV, name = "n_files") %>%
    arrange(CV)

  counts_by_trait <- combined_inventory %>%
    count(CV, Trait, name = "n_files") %>%
    arrange(CV, Trait)
}

write_csv(counts_by_cv, file.path(analysis_tables_dir, "analysis_prediction_counts_by_cv.csv"))
write_csv(counts_by_trait, file.path(analysis_tables_dir, "analysis_prediction_counts_by_trait.csv"))

if (nrow(counts_by_cv) > 0) {
  knitr::kable(counts_by_cv, caption = "Número de arquivos de predição por esquema de validação.", align = "cc")
} else {
  cat("As contagens de predição por esquema de validação ainda não estão disponíveis.
")
}

Número de arquivos de predição por esquema de validação.

CV	n_files
CV0	7200
CV00	7200
CV1	1800
CV2	1800

11. Exemplos do padrão de caminhos das saídas

output_examples <- bind_rows(
  tibble::tibble(
    ExampleType = "CV1/CV2",
    relative_path = c(
      "CV1/GY/rep01/fold01/CV1_GY_Eta1_rep01_fold01.csv",
      "CV2/KW/rep10/fold05/CV2_KW_Eta18_rep10_fold05.csv"
    ),
    bglr_example = c(
      "bglr_runs/CV1/GY/rep01/fold01/Eta1/CV1_GY_Eta1_rep01_fold01_GxE_RKHS_varU.dat",
      "bglr_runs/CV2/KW/rep10/fold05/Eta18/CV2_KW_Eta18_rep10_fold05_PAKW_RKHS_varU.dat"
    ),
    Meaning = c(
      "Acurácia preditiva para CV1, trait GY, modelo Eta1, repetição 1, fold 1.",
      "Acurácia preditiva para CV2, trait KW, modelo Eta18, repetição 10, fold 5."
    )
  ),
  tibble::tibble(
    ExampleType = "CV0/CV00",
    relative_path = c(
      "CV0/CS11_WS/GY/rep01/fold01/CV0_GY_CS11_WS_Eta1_rep01_fold01.csv",
      "CV00/CS12_WW/KW/rep10/fold02/CV00_KW_CS12_WW_Eta18_rep10_fold02.csv"
    ),
    bglr_example = c(
      "bglr_runs/CV0/CS11_WS/GY/rep01/fold01/Eta1/CV0_CS11_WS_GY_Eta1_rep01_fold01_GxW_RKHS_varU.dat",
      "bglr_runs/CV00/CS12_WW/KW/rep10/fold02/Eta18/CV00_CS12_WW_KW_Eta18_rep10_fold02_PAKW_RKHS_varU.dat"
    ),
    Meaning = c(
      "Acurácia preditiva para CV0, trait GY, ambiente retirado CS11_WS, modelo Eta1, repetição 1, fold 1.",
      "Acurácia preditiva para CV00, trait KW, ambiente retirado CS12_WW, modelo Eta18, repetição 10, fold 2."
    )
  )
)

write_csv(output_examples, file.path(analysis_tables_dir, "analysis_prediction_output_examples.csv"))
knitr::kable(output_examples, caption = "Exemplos de arquivos de saída armazenados no layout atual por repetição e fold, juntamente com os prefixes específicos de cenário usados nos artefatos persistentes do BGLR.", align = "c")

Exemplos de arquivos de saída armazenados no layout atual por repetição e fold, juntamente com os prefixes específicos de cenário usados nos artefatos persistentes do BGLR.

ExampleType	relative_path	bglr_example	Meaning
CV1/CV2	CV1/GY/rep01/fold01/CV1_GY_Eta1_rep01_fold01.csv	bglr_runs/CV1/GY/rep01/fold01/Eta1/CV1_GY_Eta1_rep01_fold01_GxE_RKHS_varU.dat	Acurácia preditiva para CV1, trait GY, modelo Eta1, repetição 1, fold 1.
CV1/CV2	CV2/KW/rep10/fold05/CV2_KW_Eta18_rep10_fold05.csv	bglr_runs/CV2/KW/rep10/fold05/Eta18/CV2_KW_Eta18_rep10_fold05_PAKW_RKHS_varU.dat	Acurácia preditiva para CV2, trait KW, modelo Eta18, repetição 10, fold 5.
CV0/CV00	CV0/CS11_WS/GY/rep01/fold01/CV0_GY_CS11_WS_Eta1_rep01_fold01.csv	bglr_runs/CV0/CS11_WS/GY/rep01/fold01/Eta1/CV0_CS11_WS_GY_Eta1_rep01_fold01_GxW_RKHS_varU.dat	Acurácia preditiva para CV0, trait GY, ambiente retirado CS11_WS, modelo Eta1, repetição 1, fold 1.
CV0/CV00	CV00/CS12_WW/KW/rep10/fold02/CV00_KW_CS12_WW_Eta18_rep10_fold02.csv	bglr_runs/CV00/CS12_WW/KW/rep10/fold02/Eta18/CV00_CS12_WW_KW_Eta18_rep10_fold02_PAKW_RKHS_varU.dat	Acurácia preditiva para CV00, trait KW, ambiente retirado CS12_WW, modelo Eta18, repetição 10, fold 2.

12. Nota sobre artefatos temporários do BGLR

No workflow atual de 18 modelos, os artefatos persistentes de saveAt são escritos em output/results/bglr_runs/ durante cada ajuste e preservados para futuras análises de convergência. Por isso, este módulo de execução inventaria tanto os CSVs finais armazenados em output/results/ quanto os caminhos hierárquicos que localizam as pastas correspondentes de execução do BGLR.

13. Nota final

Este arquivo fecha a camada de execução da etapa de predição preservando a consistência com o módulo reduzido de pré-processamento de 18 modelos documentado em analysis_prediction_pt.Rmd e com o contrato atual de CSVs usado pelos resumos posteriores.

Navegação entre etapas

Etapa anterior: Predição — Parte I
Próxima etapa: Visualização

Session information

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
[5] LC_TIME=Portuguese_Brazil.utf8    

time zone: America/Sao_Paulo
tzcode source: internal

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] tidyr_1.3.2       doParallel_1.0.17 iterators_1.0.14  foreach_1.5.2    
[5] BGLR_1.1.4        knitr_1.50        stringr_1.6.0     dplyr_1.2.0      
[9] readr_2.2.0      

loaded via a namespace (and not attached):
 [1] sass_0.4.10       generics_0.1.4    stringi_1.8.7     hms_1.1.4        
 [5] digest_0.6.39     magrittr_2.0.4    evaluate_1.0.5    fastmap_1.2.0    
 [9] rprojroot_2.1.1   workflowr_1.7.2   jsonlite_2.0.0    whisker_0.4.1    
[13] promises_1.5.0    purrr_1.2.1       truncnorm_1.0-9   codetools_0.2-20 
[17] jquerylib_0.1.4   cli_3.6.5         rlang_1.1.7       crayon_1.5.3     
[21] bit64_4.6.0-1     cachem_1.1.0      yaml_2.3.12       otel_0.2.0       
[25] tools_4.5.1       tzdb_0.5.0        httpuv_1.6.16     vctrs_0.7.1      
[29] R6_2.6.1          lifecycle_1.0.5   git2r_0.36.2      fs_1.6.6         
[33] bit_4.6.0         vroom_1.7.0       MASS_7.3-65       pkgconfig_2.0.3  
[37] pillar_1.11.1     bslib_0.9.0       later_1.4.4       glue_1.8.0       
[41] Rcpp_1.1.1        xfun_0.54         tibble_3.3.1      tidyselect_1.2.1 
[45] rstudioapi_0.17.1 htmltools_0.5.9   rmarkdown_2.30    compiler_4.5.1