Componentes de Variância

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Last updated: 2026-04-08

Checks: 6 1

Knit directory: Integrating-nir-genomic-kernel/

This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

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

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 7a97272

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 7a97272. 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

Este chunk inicializa a sessão e as opções padrão do documento para o módulo de componentes de variância. Assim, as análises seguintes partem de um ambiente controlado e reprodutível.

1. Introdução

Este tutorial resume os componentes de variância dos modelos preditivos usados neste estudo. No fluxo atual, este arquivo é usado principalmente para consolidar e interpretar resultados que já foram previamente estimados, embora ainda preserve a estrutura de código necessária para ajustar os modelos quando essa etapa precisar ser refeita.

O objetivo é quantificar quanto da variância fenotípica total é atribuída aos efeitos principais e aos termos de interação incluídos em cada modelo. Em termos práticos, este script ajuda a responder perguntas como:

• Quanto da variância está associado ao efeito categórico de ambiente (E)?
• Quanto é capturado pelos kernels genômicos?
• Quanto é capturado pelos kernels fenômicos/NIRS (P)?
• Quanto está associado ao kernel climático (W), usado principalmente nos modelos Eta10-Eta18?
• Quão relevantes são os termos de interação como G × E, G × W, P × E e P × W?

Este script está organizado em quatro estágios:

1. Carregar os kernels e os dados fenotípicos produzidos em matrizes.Rmd.
2. Definir os 18 modelos de componentes de variância.
3. Ler ou rerodar as saídas do BGLR, dependendo de os resumos posteriores salvos já existirem.
4. Processar e visualizar a decomposição da variância para produtividade de grãos (GY) e peso de 500 grãos (KW).

Uma grande proporção de variância associada a uma dada fonte não deve ser interpretada automaticamente como evidência de complementaridade biológica ou de melhor desempenho preditivo. Esse ponto é especialmente importante para P, porque a similaridade baseada em NIRS pode refletir forte resposta fisiológica ao ambiente além de qualquer sinal biológico estável, e para W, porque o kernel climático complementa, mas não substitui, o efeito categórico de ambiente.

Entradas, saídas e próxima etapa

Entradas desta etapa - matrizes e kernels gerados em matrizes.Rmd - o catálogo reduzido de 18 modelos usado no projeto

Saídas desta etapa - resultados brutos de componentes de variância - resumos processados de componentes de variância - tabelas percentuais e figuras diagnósticas

Próxima etapa - esta etapa complementa a interpretação estrutural dos modelos e deve ser lida em conjunto com os resultados preditivos gerados depois em analysis_prediction_pt.Rmd.

2. Ambiente em R e entradas necessárias

Primeiro, carregamos os pacotes usados para manipular os dados, ajustar os modelos Bayesianos e gerar as figuras finais em padrão de artigo.

Este chunk carrega os pacotes usados para ajuste de modelos, manipulação de dados, tabelas e gráficos. Reunir esses pacotes aqui facilita entender a estrutura do módulo.

library(dplyr)
library(tidyr)
library(readr)
library(stringr)
library(tibble)
library(ggplot2)
library(BGLR)
library(parallel)
library(doParallel)
library(foreach)
library(ggthemes)
library(scales)
library(fs)
library(grid)
library(knitr)
library(kableExtra)

2.1. Caminhos dos arquivos

Todos os kernels e objetos fenotípicos devem estar disponíveis em output/Matrizes/, que é o diretório de saída criado em matrizes.Rmd. Os resultados de componentes de variância gerados aqui são armazenados em output/variance_components/.

Este chunk define as pastas usadas para ler as matrizes e salvar as saídas do módulo de componentes de variância. Deixar os caminhos explícitos ajuda a entender de onde vêm as entradas e para onde vão os resultados.

kernel_dir <- "output/Matrizes"
results_dir <- "output/variance_components"
figure_dir <- file.path(results_dir, "figures")
bglr_runs_dir <- file.path(results_dir, "bglr_runs")

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

2.2. Checagem das entradas

Antes de ler os arquivos, verificamos se os objetos .rds esperados estão presentes na pasta de saídas das matrizes. Isso deixa o tutorial mais robusto porque o script para imediatamente se algum objeto necessário estiver ausente.

Este chunk verifica se todas as matrizes e arquivos de apoio necessários estão disponíveis. O objetivo é interromper cedo caso os módulos anteriores ainda não tenham produzido os objetos exigidos por esta etapa.

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

missing_files <- required_files[!file.exists(file.path(kernel_dir, required_files))]

if (length(missing_files) > 0) {
  stop(
    "The following required files were not found in output/Matrizes/: ",
    paste(missing_files, collapse = ", "),
    ". Run matrizes.Rmd first."
  )
}

2.3. Rótulos usados nas tabelas e figuras finais

Esses vetores definem como os componentes de variância aparecerão nas saídas finais. Manter esses rótulos visíveis no script principal ajuda o leitor a entender como os nomes internos do modelo são traduzidos para rótulos prontos para artigo.

Este chunk define os rótulos usados para traduzir os componentes dos modelos em nomes de saída mais legíveis. Esses rótulos são importantes porque tornam os resumos de variância mais fáceis de interpretar.

component_display <- c(
  E = "Ambiente (E)",
  G = "Genômico linear (G)",
  P = "Fenômico linear (P)",
  W = "Kernel climático (W)",
  GE = "G × E",
  PE = "P × E",
  GW = "G × W",
  PW = "P × W",
  GGK = "Genômico Gaussiano (GGK)",
  PGK = "Fenômico Gaussiano (PGK)",
  GAK = "Genômico arc-cosine (GAK)",
  PAK = "Fenômico arc-cosine (PAK)",
  GGKE = "GGK × E",
  PGKE = "PGK × E",
  GAKE = "GAK × E",
  PAKE = "PAK × E",
  GGKW = "GGK × W",
  PGKW = "PGK × W",
  GAKW = "GAK × W",
  PAKW = "PAK × W",
  Residual = "Residual"
)

component_order <- c(
  "Ambiente (E)",
  "Genômico linear (G)",
  "Fenômico linear (P)",
  "Kernel climático (W)",
  "G × E",
  "P × E",
  "G × W",
  "P × W",
  "Genômico Gaussiano (GGK)",
  "Fenômico Gaussiano (PGK)",
  "Genômico arc-cosine (GAK)",
  "Fenômico arc-cosine (PAK)",
  "GGK × E",
  "PGK × E",
  "GAK × E",
  "PAK × E",
  "GGK × W",
  "PGK × W",
  "GAK × W",
  "PAKW × W",
  "Residual"
)

component_order[component_order == "PAKW × W"] <- "PAK × W"

3. Carregamento dos kernels e objetos fenotípicos

Esta seção carrega as matrizes geradas em matrizes.Rmd. Esses objetos são as entradas centrais da análise de componentes de variância.

Este chunk restaura as matrizes e os kernels que entram nos modelos de componentes de variância. Uma vez carregados, esses objetos fornecem o conjunto completo de fontes cuja contribuição será resumida.

3.1. Diagnósticos de alinhamento

Antes de ajustar os modelos, é importante verificar se os kernels são quadrados e alinhados à mesma ordem de observações usada em Pheno. Em um contexto tutorial, essa é uma checagem importante porque muitos erros posteriores são causados por ordens incompatíveis entre linhas e colunas.

Este chunk realiza verificações estruturais para confirmar que as matrizes carregadas estão dimensionalmente alinhadas. Essas checagens ajudam a garantir que os componentes possam ser combinados sem problemas ocultos de indexação.

reference_ids <- Pheno$ObsID

ZE <- ZE[match(reference_ids, rownames(ZE)), , drop = FALSE]

ZG   <- ZG[reference_ids, reference_ids, drop = FALSE]
ZP   <- ZP[reference_ids, reference_ids, drop = FALSE]
ZEZE <- ZEZE[reference_ids, reference_ids, drop = FALSE]
ZW   <- ZW[reference_ids, reference_ids, drop = FALSE]
ZGZE <- ZGZE[reference_ids, reference_ids, drop = FALSE]
ZPZE <- ZPZE[reference_ids, reference_ids, drop = FALSE]
ZGZW <- ZGZW[reference_ids, reference_ids, drop = FALSE]
ZPZW <- ZPZW[reference_ids, reference_ids, drop = FALSE]
GGK  <- GGK[reference_ids, reference_ids, drop = FALSE]
PGK  <- PGK[reference_ids, reference_ids, drop = FALSE]
GAK  <- GAK[reference_ids, reference_ids, drop = FALSE]
PAK  <- PAK[reference_ids, reference_ids, drop = FALSE]
GGKE <- GGKE[reference_ids, reference_ids, drop = FALSE]
PGKE <- PGKE[reference_ids, reference_ids, drop = FALSE]
GGKW <- GGKW[reference_ids, reference_ids, drop = FALSE]
PGKW <- PGKW[reference_ids, reference_ids, drop = FALSE]
GAKE <- GAKE[reference_ids, reference_ids, drop = FALSE]
PAKE <- PAKE[reference_ids, reference_ids, drop = FALSE]
GAKW <- GAKW[reference_ids, reference_ids, drop = FALSE]
PAKW <- PAKW[reference_ids, reference_ids, drop = FALSE]

kernel_inventory <- tibble(
  kernel = c("ZG", "ZP", "ZEZE", "ZW", "GGK", "PGK", "GAK", "PAK"),
  n_rows = c(nrow(ZG), nrow(ZP), nrow(ZEZE), nrow(ZW), nrow(GGK), nrow(PGK), nrow(GAK), nrow(PAK)),
  n_cols = c(ncol(ZG), ncol(ZP), ncol(ZEZE), ncol(ZW), ncol(GGK), ncol(PGK), ncol(GAK), ncol(PAK)),
  is_square = c(
    nrow(ZG) == ncol(ZG),
    nrow(ZP) == ncol(ZP),
    nrow(ZEZE) == ncol(ZEZE),
    nrow(ZW) == ncol(ZW),
    nrow(GGK) == ncol(GGK),
    nrow(PGK) == ncol(PGK),
    nrow(GAK) == ncol(GAK),
    nrow(PAK) == ncol(PAK)
  ),
  symmetric_names = c(
    identical(rownames(ZG), colnames(ZG)),
    identical(rownames(ZP), colnames(ZP)),
    identical(rownames(ZEZE), colnames(ZEZE)),
    identical(rownames(ZW), colnames(ZW)),
    identical(rownames(GGK), colnames(GGK)),
    identical(rownames(PGK), colnames(PGK)),
    identical(rownames(GAK), colnames(GAK)),
    identical(rownames(PAK), colnames(PAK))
  ),
  aligned_with_reference = c(
    identical(rownames(ZG), reference_ids),
    identical(rownames(ZP), reference_ids),
    identical(rownames(ZEZE), reference_ids),
    identical(rownames(ZW), reference_ids),
    identical(rownames(GGK), reference_ids),
    identical(rownames(PGK), reference_ids),
    identical(rownames(GAK), reference_ids),
    identical(rownames(PAK), reference_ids)
  )
)

kernel_inventory %>%
  dplyr::mutate(
    n_rows = format(n_rows, big.mark = ","),
    n_cols = format(n_cols, big.mark = ",")
  ) %>%
  knitr::kable(
    caption = "Resumo diagnóstico dos principais kernels usados na análise de componentes de variância após alinhamento explícito à ordem das observações fenotípicas.",
    align = "lcccccc"
  ) %>%
  kableExtra::kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = FALSE,
    position = "left"
  ) %>%
  kableExtra::scroll_box(width = "100%", height = "260px")

Resumo diagnóstico dos principais kernels usados na análise de componentes de variância após alinhamento explícito à ordem das observações fenotípicas.

kernel	n_rows	n_cols	is_square	symmetric_names	aligned_with_reference
ZG	982	982	TRUE	TRUE	TRUE
ZP	982	982	TRUE	TRUE	TRUE
ZEZE	982	982	TRUE	TRUE	TRUE
ZW	982	982	TRUE	TRUE	TRUE
GGK	982	982	TRUE	TRUE	TRUE
PGK	982	982	TRUE	TRUE	TRUE
GAK	982	982	TRUE	TRUE	TRUE
PAK	982	982	TRUE	TRUE	TRUE

4. Definição dos 18 modelos preditivos

Cada modelo é definido como uma lista no formato exigido pelo argumento ETA de BGLR. O termo de ambiente (E) é incluído como uma matriz de efeitos fixos com model = "BRR", enquanto os kernels são ajustados como efeitos aleatórios com model = "RKHS".

Este chunk define as 18 estruturas de modelo usadas na decomposição de variância. Ele espelha o catálogo empregado na predição para que a interpretação dos componentes permaneça consistente entre os módulos.

models <- list()

models[[1]] <- list(
  E  = list(X = ZE, model = "BRR"),
  G  = list(K = ZG, model = "RKHS"),
  GE = list(K = ZGZE, model = "RKHS")
)

models[[2]] <- list(
  E  = list(X = ZE, model = "BRR"),
  P  = list(K = ZP, model = "RKHS"),
  PE = list(K = ZPZE, model = "RKHS")
)

models[[3]] <- list(
  E    = list(X = ZE, model = "BRR"),
  GGK  = list(K = GGK, model = "RKHS"),
  GGKE = list(K = GGKE, model = "RKHS")
)

models[[4]] <- list(
  E    = list(X = ZE, model = "BRR"),
  PGK  = list(K = PGK, model = "RKHS"),
  PGKE = list(K = PGKE, model = "RKHS")
)

models[[5]] <- list(
  E    = list(X = ZE, model = "BRR"),
  GAK  = list(K = GAK, model = "RKHS"),
  GAKE = list(K = GAKE, model = "RKHS")
)

models[[6]] <- list(
  E    = list(X = ZE, model = "BRR"),
  PAK  = list(K = PAK, model = "RKHS"),
  PAKE = list(K = PAKE, model = "RKHS")
)

models[[7]] <- list(
  E  = list(X = ZE, model = "BRR"),
  G  = list(K = ZG, model = "RKHS"),
  P  = list(K = ZP, model = "RKHS"),
  GE = list(K = ZGZE, model = "RKHS"),
  PE = list(K = ZPZE, model = "RKHS")
)

models[[8]] <- list(
  E    = list(X = ZE, model = "BRR"),
  GGK  = list(K = GGK, model = "RKHS"),
  PGK  = list(K = PGK, model = "RKHS"),
  GGKE = list(K = GGKE, model = "RKHS"),
  PGKE = list(K = PGKE, model = "RKHS")
)

models[[9]] <- list(
  E    = list(X = ZE, model = "BRR"),
  GAK  = list(K = GAK, model = "RKHS"),
  PAK  = list(K = PAK, model = "RKHS"),
  GAKE = list(K = GAKE, model = "RKHS"),
  PAKE = list(K = PAKE, model = "RKHS")
)

models[[10]] <- list(
  E  = list(X = ZE, model = "BRR"),
  W  = list(K = ZW, model = "RKHS"),
  G  = list(K = ZG, model = "RKHS"),
  GE = list(K = ZGZE, model = "RKHS"),
  GW = list(K = ZGZW, model = "RKHS")
)

models[[11]] <- list(
  E  = list(X = ZE, model = "BRR"),
  W  = list(K = ZW, model = "RKHS"),
  P  = list(K = ZP, model = "RKHS"),
  PE = list(K = ZPZE, model = "RKHS"),
  PW = list(K = ZPZW, model = "RKHS")
)

models[[12]] <- list(
  E  = list(X = ZE, model = "BRR"),
  W  = list(K = ZW, model = "RKHS"),
  G  = list(K = ZG, model = "RKHS"),
  P  = list(K = ZP, model = "RKHS"),
  GE = list(K = ZGZE, model = "RKHS"),
  PE = list(K = ZPZE, model = "RKHS"),
  GW = list(K = ZGZW, model = "RKHS"),
  PW = list(K = ZPZW, model = "RKHS")
)

models[[13]] <- list(
  E    = list(X = ZE, model = "BRR"),
  W    = list(K = ZW, model = "RKHS"),
  GGK  = list(K = GGK, model = "RKHS"),
  GGKE = list(K = GGKE, model = "RKHS"),
  GGKW = list(K = GGKW, model = "RKHS")
)

models[[14]] <- list(
  E    = list(X = ZE, model = "BRR"),
  W    = list(K = ZW, model = "RKHS"),
  PGK  = list(K = PGK, model = "RKHS"),
  PGKE = list(K = PGKE, model = "RKHS"),
  PGKW = list(K = PGKW, model = "RKHS")
)

models[[15]] <- list(
  E    = list(X = ZE, model = "BRR"),
  W    = list(K = ZW, model = "RKHS"),
  GGK  = list(K = GGK, model = "RKHS"),
  PGK  = list(K = PGK, model = "RKHS"),
  GGKE = list(K = GGKE, model = "RKHS"),
  PGKE = list(K = PGKE, model = "RKHS"),
  GGKW = list(K = GGKW, model = "RKHS"),
  PGKW = list(K = PGKW, model = "RKHS")
)

models[[16]] <- list(
  E    = list(X = ZE, model = "BRR"),
  W    = list(K = ZW, model = "RKHS"),
  GAK  = list(K = GAK, model = "RKHS"),
  GAKE = list(K = GAKE, model = "RKHS"),
  GAKW = list(K = GAKW, model = "RKHS")
)

models[[17]] <- list(
  E    = list(X = ZE, model = "BRR"),
  W    = list(K = ZW, model = "RKHS"),
  PAK  = list(K = PAK, model = "RKHS"),
  PAKE = list(K = PAKE, model = "RKHS"),
  PAKW = list(K = PAKW, model = "RKHS")
)

models[[18]] <- list(
  E    = list(X = ZE, model = "BRR"),
  W    = list(K = ZW, model = "RKHS"),
  GAK  = list(K = GAK, model = "RKHS"),
  PAK  = list(K = PAK, model = "RKHS"),
  GAKE = list(K = GAKE, model = "RKHS"),
  PAKE = list(K = PAKE, model = "RKHS"),
  GAKW = list(K = GAKW, model = "RKHS"),
  PAKW = list(K = PAKW, model = "RKHS")
)

names(models) <- paste0("Eta", seq_along(models))

model_component_lookup <- bind_rows(
  lapply(seq_along(models), function(i) {
    tibble(
      ModelID = i,
      EtaIndex = seq_along(models[[i]]),
      ComponentBase = names(models[[i]])
    )
  })
)

4.1. Catálogo de modelos

Esta tabela é útil tanto para verificações internas quanto para vincular os rótulos das figuras à descrição formal de cada modelo.

Este chunk constrói uma descrição tabular do catálogo de modelos usado na análise de variância. A tabela serve como guia ao interpretar os resumos e figuras produzidos adiante.

model_info <- tibble(
  ModelID = 1:18,
  Model = paste0("Eta", 1:18),
  ETA = names(models),
  Categoria = c(
    rep("Fonte única + E", 6),
    rep("Integrado G/P + E", 3),
    rep("Enriquecido com clima", 9)
  ),
  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"
  )
)

readr::write_csv(model_info, file.path(results_dir, "model_catalog.csv"))

model_info %>%
  knitr::kable(
    caption = "Catálogo dos 18 modelos avaliados na análise de componentes de variância.",
    align = "cll"
  ) %>%
  kableExtra::kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = FALSE,
    position = "left"
  ) %>%
  kableExtra::scroll_box(width = "100%", height = "340px")

Catálogo dos 18 modelos avaliados na análise de componentes de variância.

ModelID	Model	ETA	Categoria	Description
1	Eta1	Eta1	Fonte única + E	E + G + GE
2	Eta2	Eta2	Fonte única + E	E + P + PE
3	Eta3	Eta3	Fonte única + E	E + GGK + GGKE
4	Eta4	Eta4	Fonte única + E	E + PGK + PGKE
5	Eta5	Eta5	Fonte única + E	E + GAK + GAKE
6	Eta6	Eta6	Fonte única + E	E + PAK + PAKE
7	Eta7	Eta7	Integrado G/P + E	E + G + P + GE + PE
8	Eta8	Eta8	Integrado G/P + E	E + GGK + PGK + GGKE + PGKE
9	Eta9	Eta9	Integrado G/P + E	E + GAK + PAK + GAKE + PAKE
10	Eta10	Eta10	Enriquecido com clima	E + W + G + GE + GW
11	Eta11	Eta11	Enriquecido com clima	E + W + P + PE + PW
12	Eta12	Eta12	Enriquecido com clima	E + W + G + P + GE + PE + GW + PW
13	Eta13	Eta13	Enriquecido com clima	E + W + GGK + GGKE + GGKW
14	Eta14	Eta14	Enriquecido com clima	E + W + PGK + PGKE + PGKW
15	Eta15	Eta15	Enriquecido com clima	E + W + GGK + PGK + GGKE + PGKE + GGKW + PGKW
16	Eta16	Eta16	Enriquecido com clima	E + W + GAK + GAKE + GAKW
17	Eta17	Eta17	Enriquecido com clima	E + W + PAK + PAKE + PAKW
18	Eta18	Eta18	Enriquecido com clima	E + W + GAK + PAK + GAKE + PAKE + GAKW + PAKW

5. Configurações do MCMC

Esses valores definem o amostrador de Gibbs usado pelo BGLR. Ajuste-os aqui se uma cadeia mais longa for necessária para diagnósticos de convergência ou análises de sensibilidade.

Este chunk registra as configurações de MCMC e os controles de repetição usados nos ajustes de componentes de variância. Esses valores definem o plano computacional da etapa de decomposição.

nIter   <- 5000
burnIn  <- 1000
thin    <- 10
num_rep <- 10
traits  <- c("GY", "KW")

6. Ajuste do BGLR para cada modelo

Este é o passo computacionalmente mais caro do fluxo.

O bloco seguinte contém a etapa paralela de estimação que ajusta os modelos de componentes de variância, preserva os artefatos iterativos do BGLR em pastas persistentes e salva os resumos posteriores brutos.

Este chunk ajusta os modelos de componentes de variância e salva os artefatos brutos do BGLR necessários para a consolidação posterior. Ele é o núcleo computacional do módulo.

numCores <- parallel::detectCores()
useCores <- max(1, numCores - 1)
cl <- parallel::makeCluster(useCores)
doParallel::registerDoParallel(cl)

combos <- expand.grid(
  Model = 1:18,
  REP = 1:num_rep,
  Trait = traits,
  stringsAsFactors = FALSE
)

results_list <- foreach(
  i = 1:nrow(combos),
  .packages = c("BGLR", "dplyr", "tibble")
) %dopar% {
  Model <- combos$Model[i]
  rep_num <- combos$REP[i]
  Trait <- combos$Trait[i]
  rep_label <- sprintf("rep%02d", rep_num)
  eta_label <- paste0("Eta", Model)

  pheno <- data.frame(Pheno)
  y_data <- as.numeric(pheno[[Trait]])

  out_dir <- file.path(bglr_runs_dir, Trait, rep_label, eta_label)
  dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)

  save_prefix <- file.path(
    out_dir,
    paste0("VC_", Trait, "_", eta_label, "_", rep_label, "_")
  )

  start_time <- Sys.time()
  fit <- BGLR(
    y = y_data,
    ETA = models[[Model]],
    nIter = nIter,
    burnIn = burnIn,
    thin = thin,
    saveAt = save_prefix
  )
  end_time <- Sys.time()

  effect_names <- names(models[[Model]])
  effect_models <- vapply(models[[Model]], function(x) x$model, character(1))
  effect_labels <- paste0(effect_names, "_", effect_models)

  generated_files <- list.files(
    out_dir,
    pattern = paste0("^", basename(save_prefix)),
    full.names = TRUE
  )

  if (length(generated_files) > 0) {
    for (current_file in generated_files) {
      current_name <- basename(current_file)
      renamed_name <- current_name

      for (j in seq_along(effect_labels)) {
        renamed_name <- sub(
          paste0("ETA_", j),
          effect_labels[j],
          renamed_name,
          fixed = TRUE
        )
      }

      if (!identical(current_name, renamed_name)) {
        file.rename(current_file, file.path(out_dir, renamed_name))
      }
    }
  }

  variance_samples <- list()

  if (!is.null(fit$ETA)) {
    if (is.null(effect_names)) {
      effect_names <- paste0("Effect_", seq_along(fit$ETA))
    }

    for (j in seq_along(fit$ETA)) {
      parameter_names <- names(fit$ETA[[j]])
      var_parameter <- parameter_names[grepl("^var", parameter_names)]

      if (length(var_parameter) > 0) {
        variance_samples[[effect_names[j]]] <- fit$ETA[[j]][[var_parameter[1]]]
      }
    }
  }

  metrics_object <- list(
    varE_samples = fit$varE,
    variances_samples = variance_samples,
    DIC = fit$fit$DIC,
    runtime_sec = as.numeric(difftime(end_time, start_time, units = "secs"))
  )

  saveRDS(metrics_object, file.path(out_dir, "variance_metrics.rds"))

  result_table <- tibble(
    Component = "Residual",
    MeanVar = mean(metrics_object$varE_samples, na.rm = TRUE)
  )

  if (length(metrics_object$variances_samples) > 0) {
    for (nm in names(metrics_object$variances_samples)) {
      result_table <- bind_rows(
        result_table,
        tibble(
          Component = nm,
          MeanVar = mean(metrics_object$variances_samples[[nm]], na.rm = TRUE)
        )
      )
    }
  }

  rm(fit)
  gc()

  result_table %>%
    mutate(Model = Model, Rep = rep_num, Trait = Trait)
}

stopCluster(cl)

all_variance <- bind_rows(results_list)
write_csv(all_variance, file.path(results_dir, "variance_components_raw.csv"))
saveRDS(all_variance, file.path(results_dir, "variance_components_raw.rds"))

7. Consolidação dos resumos posteriores salvos

Na versão atual do fluxo, esta é a seção usada com mais frequência. Em vez de reajustar todos os modelos a cada execução, o script lê os arquivos _metrics.rds previamente salvos e os consolida em um único objeto. Isso mantém o tutorial prático e reprodutível, ao mesmo tempo que deixa claro que esta página é principalmente uma etapa de consolidação de resultados, a menos que o usuário decida rerodar intencionalmente a etapa completa de ajuste no BGLR.

Este chunk reúne as saídas salvas durante os ajustes e as reorganiza em tabelas prontas para análise. O objetivo é fazer a ponte entre os artefatos brutos e os resumos finais.

rds_files <- list.files(
  path = bglr_runs_dir,
  pattern = "(variance_metrics|_(metrics|metricas))\\.rds$",
  recursive = TRUE,
  full.names = TRUE
)

if (length(rds_files) > 0) {
  all_tables <- vector("list", length(rds_files))

  for (i in seq_along(rds_files)) {
    current_file <- normalizePath(rds_files[i], winslash = "/", mustWork = FALSE)
    path_parts <- str_split(current_file, "/")[[1]]

    rep_part <- path_parts[str_detect(path_parts, "^rep[0-9]+$|^rep_[0-9]+$")][1]
    eta_part <- path_parts[str_detect(path_parts, "^Eta")][1]
    trait_part <- path_parts[path_parts %in% traits][1]

    rep_value <- as.integer(str_remove(rep_part, "^rep_?0*"))
    model_value <- as.integer(str_remove(eta_part, "^Eta0*"))
    trait_value <- trait_part

    current_object <- readRDS(current_file)

    current_table <- tibble(
      Rep = rep_value,
      Model = model_value,
      Trait = trait_value,
      Component = "Residual",
      MeanVar = mean(current_object$varE_samples, na.rm = TRUE),
      DIC = current_object$DIC,
      runtime_sec = current_object$runtime_sec
    )

    if (length(current_object$variances_samples) > 0) {
      for (nm in names(current_object$variances_samples)) {
        current_table <- bind_rows(
          current_table,
          tibble(
            Rep = rep_value,
            Model = model_value,
            Trait = trait_value,
            Component = {
              current_component <- nm
              if (grepl("^ETA_[0-9]+$", current_component)) {
                current_eta_index <- suppressWarnings(as.integer(sub("^ETA_", "", current_component)))
                current_match <- which(
                  model_component_lookup$ModelID == model_value &
                    model_component_lookup$EtaIndex == current_eta_index
                )
                if (length(current_match) == 1) {
                  current_component <- model_component_lookup$ComponentBase[current_match]
                }
              }
              current_component <- sub("_(BRR|RKHS)$", "", current_component)
              current_component
            },
            MeanVar = mean(current_object$variances_samples[[nm]], na.rm = TRUE),
            DIC = current_object$DIC,
            runtime_sec = current_object$runtime_sec
          )
        )
      }
    }

    all_tables[[i]] <- current_table
  }

  all_variance <- bind_rows(all_tables)

  saveRDS(all_variance, file.path(results_dir, "variance_components_all.rds"))
  write_csv(all_variance, file.path(results_dir, "variance_components_raw.csv"))
}

8. Carregamento do objeto consolidado de componentes de variância

Esta seção carrega os resultados consolidados e os prepara para o resumo. Se o arquivo não existir, o script interrompe a execução e instrui o usuário a executar a etapa de ajuste dos modelos primeiro.

Este chunk restaura os resultados consolidados de variância para que as seções seguintes se concentrem no processamento e na interpretação. Assim, a parte explicativa do documento permanece centrada nas saídas finais.

9. Resumo dos componentes de variância

Agora calculamos a média dos valores posteriores ao longo das repetições para cada modelo, caráter e componente. Em seguida, convertemos os resultados em percentuais da variância total, o que torna os modelos diretamente comparáveis.

Este chunk calcula os resumos processados que expressam quanto da variância é atribuído a cada componente do modelo. Esses resumos transformam as quantidades brutas salvas em resultados de decomposição interpretáveis.

variance_summary <- all_variance %>%
  group_by(Model, Trait, Component) %>%
  summarise(
    AvgVar = mean(MeanVar, na.rm = TRUE),
    MeanDIC = mean(DIC, na.rm = TRUE),
    MeanRuntimeSec = mean(runtime_sec, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  filter(!is.na(AvgVar), AvgVar > 0) %>%
  group_by(Model, Trait) %>%
  mutate(
    TotalVar = sum(AvgVar),
    Percentage = AvgVar / TotalVar
  ) %>%
  ungroup() %>%
  left_join(model_info, by = c("Model" = "ModelID")) %>%
  mutate(
    ModelNumber = Model,
    EtaIndex = ifelse(grepl("^ETA_[0-9]+$", Component),
                      suppressWarnings(as.integer(sub("^ETA_", "", Component))),
                      NA_integer_),
    Component = ifelse(
      !is.na(EtaIndex),
      model_component_lookup$ComponentBase[
        match(
          paste(ModelNumber, EtaIndex),
          paste(model_component_lookup$ModelID, model_component_lookup$EtaIndex)
        )
      ],
      Component
    ),
    Component = sub("_(BRR|RKHS)$", "", Component),
    Model = factor(paste0("Eta", ModelNumber), levels = paste0("Eta", 1:18)),
    ComponentLabel = dplyr::recode(Component, !!!component_display),
    ComponentLabel = factor(ComponentLabel, levels = component_order),
    Trait = factor(Trait, levels = c("GY", "KW"))
  ) %>%
  dplyr::select(-ModelNumber, -EtaIndex)

readr::write_csv(variance_summary, file.path(results_dir, "variance_components_processed.csv"))

9.1. Tabela resumo em formato amplo

Esta tabela é útil quando você precisa de um resumo numérico compacto para o manuscrito ou para material suplementar.

Este chunk monta a tabela principal de resumo da decomposição de variância. A tabela oferece uma visão numérica compacta da contribuição dos componentes ao longo dos modelos.

variance_table <- variance_summary %>%
  dplyr::select(Model, Trait, ComponentLabel, Percentage) %>%
  dplyr::mutate(Percentage = round(100 * Percentage, 2)) %>%
  tidyr::pivot_wider(names_from = ComponentLabel, values_from = Percentage)

readr::write_csv(variance_table, file.path(results_dir, "variance_components_table_percent.csv"))

head(variance_table, 12) %>%
  knitr::kable(
    caption = "Pré-visualização da tabela processada de componentes de variância (percentual da variância total).",
    align = "lcccccccccccc"
  ) %>%
  kableExtra::kable_styling(
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    full_width = FALSE,
    position = "left"
  ) %>%
  kableExtra::scroll_box(width = "100%", height = "320px")

Pré-visualização da tabela processada de componentes de variância (percentual da variância total).

Model	Trait	Ambiente (E)	Genômico linear (G)	G × E	Residual	Fenômico linear (P)	P × E	Genômico Gaussiano (GGK)	GGK × E	Fenômico Gaussiano (PGK)	PGK × E	Genômico arc-cosine (GAK)	GAK × E	Fenômico arc-cosine (PAK)	PAK × E	G × W	Kernel climático (W)	P × W	GGK × W	PGK × W	GAK × W	PAK × W
Eta1	GY	83.59	11.96	1.70	2.75	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta1	KW	75.42	20.80	1.46	2.33	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta2	GY	0.00	NA	NA	0.00	100	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta2	KW	0.00	NA	NA	0.00	100	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta3	GY	49.52	NA	NA	1.92	NA	NA	45.13	3.43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta3	KW	35.42	NA	NA	1.26	NA	NA	61.28	2.04	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta4	GY	78.05	NA	NA	10.87	NA	NA	NA	NA	7.54	3.53	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta4	KW	73.38	NA	NA	11.51	NA	NA	NA	NA	11.07	4.04	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta5	GY	5.49	NA	NA	0.26	NA	NA	NA	NA	NA	NA	92.52	1.72	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta5	KW	3.38	NA	NA	0.16	NA	NA	NA	NA	NA	NA	95.71	0.75	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eta6	GY	18.38	NA	NA	3.26	NA	NA	NA	NA	NA	NA	NA	NA	77.06	1.30	NA	NA	NA	NA	NA	NA	NA
Eta6	KW	10.60	NA	NA	2.14	NA	NA	NA	NA	NA	NA	NA	NA	86.37	0.89	NA	NA	NA	NA	NA	NA	NA

10. Figura para o artigo

A figura abaixo resume a contribuição percentual de cada componente de variância nos 18 modelos para ambos os caracteres. Para manter o eixo x legível, os modelos são exibidos como Eta1 a Eta18, e as descrições detalhadas permanecem disponíveis no catálogo de modelos salvo acima.

Essa figura deve ser lida como um resumo estrutural dos modelos ajustados, e não como prova direta de que combinar mais fontes de dados leva automaticamente a maior complementaridade biológica ou melhor desempenho preditivo.

Este chunk constrói a figura final que compara as contribuições de variância entre modelos e traits. O gráfico ajuda a interpretar como a estrutura de cada modelo redistribui a variância explicada.

trait_labels <- c(
  GY = "A. Produtividade de grãos (GY)",
  KW = "B. Peso de grãos (KW)"
)

variance_plot <- variance_summary %>%
  ggplot(aes(x = Model, y = Percentage, fill = ComponentLabel)) +
  geom_col(color = "white", linewidth = 0.15, width = 0.82) +
  facet_wrap(~ Trait, ncol = 1, labeller = as_labeller(trait_labels)) +
  ggthemes::scale_fill_gdocs(name = "Componente de variância", drop = FALSE) +
  scale_y_continuous(
    labels = scales::percent_format(accuracy = 1),
    expand = expansion(mult = c(0, 0.02))
  ) +
  labs(
    x = "Identificador do modelo",
    y = "Proporção da variância total"
  ) +
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold", size = 13, hjust = 0.5),
    strip.text = element_text(face = "bold", size = 11),
    axis.title = element_text(face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 8),
    axis.text.y = element_text(size = 9),
    legend.position = "right",
    legend.box = "vertical",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 8),
    legend.key.height = unit(0.45, "cm"),
    legend.key.width = unit(0.55, "cm"),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank()
  ) +
  guides(fill = guide_legend(ncol = 2, byrow = TRUE))

print(variance_plot)

Figura 3. Percentual médio posterior da variância total atribuído a cada componente do modelo nos 18 modelos preditivos para produtividade de grãos (GY) e peso de grãos (KW). Cada barra empilhada representa um modelo (Eta1-Eta18), com média ao longo das repetições, e as cores identificam os componentes de variância incluídos na estrutura do modelo. O kernel climático (W) e seus termos de interação associados aparecem principalmente nos modelos Eta10-Eta18. A decomposição da variância deve ser interpretada como uma descrição da estrutura do modelo, e não automaticamente como evidência de maior complementaridade biológica entre as fontes de informação.

Past versions of variance-figure-1.png

Version	Author	Date
0b2db18	WevertonGomesCosta	2026-04-07
4fb14e1	WevertonGomesCosta	2026-04-06
d1beaba	WevertonGomesCosta	2026-04-06
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
74c0c6f	WevertonGomesCosta	2026-03-31

ggsave(
  filename = file.path(figure_dir, "variance_components_article.tiff"),
  plot = variance_plot,
  width = 14,
  height = 10,
  dpi = 300,
  compression = "lzw",
  bg = "white"
)

11. Guia de interpretação

Esta seção final esclarece como a figura deve ser interpretada.

• Um grande segmento de Ambiente (E) indica que o efeito categórico de macroambiente domina a partição da variância.
• Grandes termos genômicos como G, GGK ou GAK indicam que a similaridade genômica captura uma parcela importante da variabilidade do caráter.
• Grandes termos fenômicos como P, PGK ou PAK indicam maior contribuição da similaridade derivada de NIRS, mas esses termos devem ser interpretados com cautela porque NIRS também pode refletir forte resposta fisiológica ao ambiente.
• Grandes termos relacionados ao clima como W, G × W ou P × W indicam que covariáveis derivadas do clima explicam estrutura adicional. Esses termos aparecem principalmente nos modelos Eta10-Eta18 e devem ser interpretados como complementares ao efeito categórico de ambiente, e não como substitutos de E.
• Um grande segmento de Residual indica que fontes importantes de variabilidade permanecem não explicadas pelo modelo ajustado.

No contexto deste artigo, essas decomposições de variância complementam as análises preditivas ao mostrar quais fontes de informação contribuem para a estrutura dos modelos. Entretanto, uma grande parcela de variância de um componente não deve ser lida automaticamente como evidência de maior complementaridade biológica ou de melhor habilidade preditiva.

Navegação entre etapas

Etapa anterior: Matrizes
Próxima etapa: Predição — Parte I

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] grid      parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] kableExtra_1.4.0  knitr_1.50        fs_1.6.6          scales_1.4.0     
 [5] ggthemes_5.2.0    doParallel_1.0.17 iterators_1.0.14  foreach_1.5.2    
 [9] BGLR_1.1.4        ggplot2_4.0.2     tibble_3.3.1      stringr_1.6.0    
[13] readr_2.2.0       tidyr_1.3.2       dplyr_1.2.0      

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.54          bslib_0.9.0        tzdb_0.5.0        
 [5] vctrs_0.7.1        tools_4.5.1        generics_0.1.4     pkgconfig_2.0.3   
 [9] RColorBrewer_1.1-3 S7_0.2.1           lifecycle_1.0.5    truncnorm_1.0-9   
[13] compiler_4.5.1     farver_2.1.2       git2r_0.36.2       textshaping_1.0.4 
[17] codetools_0.2-20   httpuv_1.6.16      htmltools_0.5.9    sass_0.4.10       
[21] yaml_2.3.12        later_1.4.4        pillar_1.11.1      crayon_1.5.3      
[25] jquerylib_0.1.4    whisker_0.4.1      MASS_7.3-65        cachem_1.1.0      
[29] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      purrr_1.2.1       
[33] labeling_0.4.3     rprojroot_2.1.1    fastmap_1.2.0      cli_3.6.5         
[37] magrittr_2.0.4     withr_3.0.2        promises_1.5.0     bit64_4.6.0-1     
[41] rmarkdown_2.30     bit_4.6.0          otel_0.2.0         workflowr_1.7.2   
[45] ragg_1.5.0         hms_1.1.4          evaluate_1.0.5     viridisLite_0.4.2 
[49] rlang_1.1.7        Rcpp_1.1.1         glue_1.8.0         xml2_1.5.1        
[53] svglite_2.2.2      rstudioapi_0.17.1  vroom_1.7.0        jsonlite_2.0.0    
[57] R6_2.6.1           systemfonts_1.3.1