Análises de predição - Parte I: setup, modelos e desenho de CV

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Last updated: 2026-04-08

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Knit directory: Integrating-nir-genomic-kernel/

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Repository version: 88e9ac3

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Language / Idioma: English | Português

Navegação do módulo / Module navigation: Parte II: execução da predição e saídas

1. Introdução

A predição é a etapa do pipeline em que os objetos produzidos anteriormente são reorganizados em uma estrutura de ajuste de modelos. Neste ponto, o objetivo ainda não é executar o fluxo completo de predição. Em vez disso, este módulo prepara a estrutura analítica que será usada mais adiante na etapa de execução.

O problema de predição estudado aqui não se limita ao ajuste de um único modelo. O projeto compara um catálogo reduzido de 18 modelos construídos a partir de diferentes combinações de informação ambiental (E), informação genômica (G), informação fenômica (P), informação derivada do clima (W) e suas correspondentes estruturas de interação lineares ou baseadas em kernels. Como esses modelos foram concebidos para responder a diferentes perguntas de predição sob diferentes contextos de validação, o primeiro passo é tornar sua organização explícita e compreensível antes que qualquer computação cara seja realizada.

Este arquivo, portanto, cumpre duas funções complementares. Primeiro, prepara as entradas e as configurações computacionais necessárias para a predição. Segundo, explica o significado analítico do catálogo de modelos e dos esquemas de validação cruzada adotados no estudo. Em termos práticos, isso significa que o leitor deve sair deste módulo entendendo o que está sendo previsto, quais fontes de informação estão sendo comparadas e como a qualidade da predição será posteriormente avaliada.

Entradas, saídas e o papel desta etapa

Esta etapa recebe como entrada os objetos criados em matrizes.Rmd. Entre eles estão a tabela de dados fenotípicos (Pheno), as estruturas de incidência que conectam observações a ambientes e clima, e os kernels lineares e não lineares derivados da informação genômica e fenômica. Em conjunto, esses objetos fornecem a representação completa necessária para definir os modelos de predição.

A principal saída deste arquivo não é uma tabela final de resultados. Em vez disso, a saída é uma estrutura organizada de predição: as configurações computacionais, o catálogo de 18 modelos, as correspondentes especificações de Eta_list, o desenho de validação e a convenção de nomes que será mais tarde usada para armazenar os resultados de predição. Em outras palavras, este módulo prepara o sistema de predição que será executado na etapa seguinte.

Essa distinção é importante por razões didáticas. Aqui o foco está em entender a estrutura da análise preditiva: o que entra em cada modelo, como os modelos diferem e o que cada cenário de validação foi desenhado para testar. A execução propriamente dita é documentada no próximo módulo, onde os arquivos de predição são gerados, inventariados e preparados para os resumos das etapas posteriores.

Entradas - Observações fenotípicas e identificadores armazenados em Pheno - Estruturas de desenho para ambiente e clima - Relações lineares baseadas em incidência - Kernels genômicos e fenômicos não lineares - Kernels de interação derivados gerados em matrizes.Rmd

Saídas - Configurações computacionais gerais para o ajuste dos modelos - O catálogo de 18 modelos usado no estudo - A estrutura completa de Eta_list que define cada modelo de predição - O desenho de validação cruzada usado para avaliar o desempenho preditivo - As regras de nomes de arquivos usadas nas etapas posteriores de execução, inventário e visualização

Etapa anterior: variance_components_pt.Rmd
Próxima etapa: analysis_prediction_run_outputs_pt.Rmd

2. Pacotes e opções

Antes de definir qualquer modelo de predição, é útil começar de uma sessão de trabalho limpa e reprodutível. Em um script orientado ao ensino, esse primeiro passo também tem uma função prática: deixar claro que a especificação dos modelos não deve depender de objetos deixados por execuções anteriores.

Nesta etapa, a ênfase ainda está na organização e não na execução. Os modelos de predição são apresentados, comparados e traduzidos para suas estruturas ETA, mas a rotina cara de ajuste fica reservada ao módulo de execução. Por essa razão, a configuração inicial aqui é intencionalmente mínima. A notação científica é desligada para melhorar a legibilidade dos valores impressos, o ambiente de trabalho é limpo para que os objetos usados adiante no arquivo sejam aqueles explicitamente carregados a partir das saídas do pipeline, e o pacote parallel é declarado porque o número de núcleos de processamento já é definido nesta etapa.

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

library(parallel)

3. Entradas geradas pelos scripts anteriores

A análise preditiva começa a partir das matrizes e kernels já gerados em matrizes.Rmd. Isso significa que a etapa atual não reconstrói do zero a representação dos dados. Em vez disso, ela importa os objetos salvos que agora servem como base analítica para a especificação dos modelos.

Esses objetos representam partes diferentes do problema. Pheno contém a estrutura em nível de observação do estudo, incluindo traits, genótipos e ambientes. ZG e ZP representam as relações lineares genômicas e fenômicas. ZE representa a estrutura categórica de ambiente, enquanto ZW carrega a informação derivada do clima. Os objetos restantes correspondem a interações e kernels não lineares que permitem comparar modelos de predição mais simples e mais flexíveis.

Ler esses objetos diretamente de output/Matrizes/ é importante por duas razões. Primeiro, isso mantém o fluxo reprodutível, fazendo a predição depender de artefatos já criados na etapa anterior. Segundo, isso permite ao leitor ver como o módulo de predição está conectado ao módulo de harmonização e construção de kernels.

Pheno <- readRDS("output/Matrizes/Pheno.rds")

ZG   <- readRDS("output/Matrizes/ZG.rds")
ZP   <- readRDS("output/Matrizes/ZP.rds")
ZE   <- readRDS("output/Matrizes/ZE.rds")
ZW   <- readRDS("output/Matrizes/ZW.rds")

ZGZE <- readRDS("output/Matrizes/ZGZE.rds")
ZPZE <- readRDS("output/Matrizes/ZPZE.rds")
ZGZW <- readRDS("output/Matrizes/ZGZW.rds")
ZPZW <- readRDS("output/Matrizes/ZPZW.rds")

GGK  <- readRDS("output/Matrizes/GGK.rds")
PGK  <- readRDS("output/Matrizes/PGK.rds")
GAK  <- readRDS("output/Matrizes/GAK.rds")
PAK  <- readRDS("output/Matrizes/PAK.rds")

GGKE <- readRDS("output/Matrizes/GGKE.rds")
PGKE <- readRDS("output/Matrizes/PGKE.rds")
GAKE <- readRDS("output/Matrizes/GAKE.rds")
PAKE <- readRDS("output/Matrizes/PAKE.rds")

GGKW <- readRDS("output/Matrizes/GGKW.rds")
PGKW <- readRDS("output/Matrizes/PGKW.rds")
GAKW <- readRDS("output/Matrizes/GAKW.rds")
PAKW <- readRDS("output/Matrizes/PAKW.rds")

Na versão atual do pipeline, as matrizes em nível de observação produzidas em matrizes.Rmd usam os nomes ZE, ZGZE, ZPZE, ZGZW e ZPZW para representar o desenho categórico de ambiente e as estruturas de interação que conectam informação genômica e fenômica a ambiente e clima. Manter esses nomes explícitos aqui ajuda o leitor a conectar os arquivos salvos às estruturas de predição que serão construídas mais adiante neste módulo.

4. MCMC e configurações gerais

Uma vez que as entradas estejam disponíveis, o próximo passo é definir as configurações computacionais que controlam o ajuste dos modelos e a validação repetida. Esses valores não mudam a pergunta científica em estudo, mas determinam como as distribuições a posteriori são exploradas e quão estáveis podem ser os resumos preditivos obtidos em repetições sucessivas.

As configurações do MCMC (nIter, burnIn e thin) definem como a estimação Bayesiana será conduzida. O número de repetições (num_rep) controla quantas partições independentes serão mais adiante usadas nos esquemas de validação. O número de núcleos de processamento (n_cores) é registrado aqui porque o módulo de execução pode usar computação paralela quando a etapa cara de predição for ativada.

nIter <- 5000
burnIn <- 1000
thin <- 10
num_rep <- 10

n_cores <- max(1, parallel::detectCores() - 1)

nIter

[1] 5000

burnIn

[1] 1000

thin

[1] 10

num_rep

[1] 10

n_cores

[1] 21

5. Catálogo de modelos

A etapa de predição não compara uma única especificação. Em vez disso, ela avalia um catálogo reduzido de 18 modelos que representam diferentes formas de combinar ambiente (E), informação genômica (G), informação fenômica (P), informação derivada do clima (W) e suas correspondentes estruturas de interação. Esse catálogo é útil porque permite ao leitor ver, em um único lugar, como a comparação analítica está organizada antes que os modelos sejam ajustados.

A tabela abaixo deve ser lida como um mapa da estratégia de modelagem adotada no estudo. Cada linha identifica um modelo, fornece um rótulo estável e resume sua composição principal. Esses rótulos serão usados novamente na etapa de execução, nos arquivos de saída e, mais adiante, no módulo de visualização. Por isso, entender o catálogo aqui torna o restante do fluxo de predição muito mais fácil de acompanhar.

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
)

model_catalog

6. Especificação dos modelos

Uma vez definido o catálogo, o passo seguinte é traduzir cada modelo para a estrutura que será passada ao BGLR. Neste fluxo, essa estrutura é armazenada em Eta_list. Cada entrada de Eta_list corresponde a um modelo do catálogo e registra quais efeitos ou kernels serão usados quando o modelo for posteriormente ajustado.

Esta parte do tutorial é importante porque conecta o significado biológico dos modelos à sua implementação computacional. O código abaixo não introduz uma nova pergunta científica; em vez disso, ele torna explícito como cada ideia analítica é codificada em uma forma que o software pode usar na etapa de execução.

6.1 Inicialização do contêiner ETA

Antes de preencher as especificações dos 18 modelos, é útil criar um contêiner vazio que armazenará essas estruturas de forma organizada. A partir daí, cada grupo de modelos é adicionado à lista na mesma ordem em que aparece no catálogo.

Eta_list <- list()

6.2 Modelos 1 a 6: vias de fonte única com E

Os primeiros seis modelos representam as comparações mais simples do catálogo. Cada um combina o efeito categórico de ambiente com uma única fonte principal de informação — linear ou baseada em kernel — juntamente com sua interação correspondente. Esses modelos são úteis como ponto de partida porque permitem ao leitor ver como cada fonte de informação se comporta quando considerada de maneira relativamente isolada.

Eta_list[[1]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = ZG,   model = "RKHS"),
                       list(K = ZGZE, model = "RKHS"))

Eta_list[[2]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = ZP,   model = "RKHS"),
                       list(K = ZPZE, model = "RKHS"))

Eta_list[[3]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = GGK,  model = "RKHS"),
                       list(K = GGKE, model = "RKHS"))

Eta_list[[4]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = PGK,  model = "RKHS"),
                       list(K = PGKE, model = "RKHS"))

Eta_list[[5]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = GAK,  model = "RKHS"),
                       list(K = GAKE, model = "RKHS"))

Eta_list[[6]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = PAK,  model = "RKHS"),
                       list(K = PAKE, model = "RKHS"))

6.3 Modelos 7 a 9: vias integradas genômicas e fenômicas sem W

O grupo seguinte combina simultaneamente duas fontes principais de informação, mas ainda sem introduzir o componente climático. Esses modelos são importantes porque permitem comparar o ganho potencial obtido ao integrar estruturas genômicas e fenômicas na mesma formulação.

Eta_list[[7]]  <- list(list(X = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), model = "BRR"),
                       list(K = GAK,  model = "RKHS"),
                       list(K = PAK,  model = "RKHS"),
                       list(K = GAKE, model = "RKHS"),
                       list(K = PAKE, model = "RKHS"))

6.4 Modelos 10 a 12: vias lineares enriquecidas com clima

Aqui o catálogo passa a incorporar explicitamente a informação derivada do clima. Esses modelos mantêm estruturas lineares para os componentes genômicos ou fenômicos, mas acrescentam W e suas interações correspondentes. Em termos didáticos, este é o ponto em que a comparação deixa de ser apenas entre fontes biológicas e passa a incluir também a contribuição potencial do clima para a predição.

Eta_list[[10]] <- list(list(X = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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"))

6.5 Modelos 13 a 18: vias não lineares enriquecidas com clima

Os seis modelos finais estendem a comparação anterior ao combinar informação derivada do clima com estruturas de kernel não lineares. Esses modelos são importantes porque representam as especificações mais flexíveis do catálogo. Em termos práticos, eles permitem ao fluxo comparar formulações lineares mais simples com alternativas não lineares mais ricas, que podem capturar padrões de similaridade mais complexos entre genótipos, perfis fenômicos e ambientes.

Eta_list[[13]] <- list(list(X = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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 = model.matrix(~ Pheno$Env - 1), 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"))

6.6 Checagem final de integridade da lista ETA

Antes de avançar para o desenho da validação, é útil verificar se a representação computacional dos modelos está completa. Essa checagem final confirma que o número de objetos ETA coincide com o número de modelos listados no catálogo. Manter essas duas estruturas alinhadas é essencial, porque as etapas seguintes assumem que rótulos de modelos, números de modelos e especificações ETA se referem ao mesmo conjunto ordenado de 18 modelos.

stopifnot(length(Eta_list) == nrow(model_catalog))
length(Eta_list)

[1] 18

7. Desenho da validação cruzada

A acurácia preditiva depende não apenas do modelo ajustado, mas também do problema de predição que está sendo imposto. Neste estudo, quatro esquemas de validação são usados para representar diferentes tipos de generalização: predição de genótipos completamente novos, predição de células ausentes de genótipo por ambiente, predição em um ambiente completamente novo e predição de novos genótipos dentro de um ambiente novo. Como esses cenários não são equivalentes, o desempenho do modelo sempre precisa ser interpretado junto com a estrutura da regra de mascaramento.

Esta seção esclarece o que é escondido em cada esquema de validação e qual pergunta científica cada um deles pretende responder. Essa distinção é essencial antes de avançar para a etapa de execução, porque o mesmo modelo pode ter desempenho muito diferente dependendo de o desafio estar no eixo dos genótipos, no eixo dos ambientes ou em ambos simultaneamente.

7.1 Cenários de validação e seu significado científico

Os quatro esquemas de validação adotados neste estudo são resumidos abaixo. Cada um corresponde a um alvo de predição diferente e, portanto, avalia uma noção diferente de generalização. Leia esta tabela como um mapa das perguntas preditivas que serão mais adiante implementadas no submódulo de execução.

cv_scenarios <- data.frame(
  CV = c("CV1", "CV2", "CV0", "CV00"),
  WhatIsMasked = c(
    "Genótipos inteiros",
    "Genótipos de treinamento sob o mesmo ajuste usado em CV1",
    "Um ambiente por vez, avaliado em genótipos já vistos",
    "Genótipos dentro de um ambiente deixado de fora"
  ),
  MainQuestion = c(
    "O modelo consegue prever genótipos completamente novos?",
    "Quão bem o mesmo ajuste de CV1 recupera os genótipos que permaneceram no treinamento?",
    "O modelo consegue prever genótipos já vistos em um ambiente completamente novo?",
    "O modelo consegue prever novos genótipos dentro de um ambiente completamente novo?"
  ),
  stringsAsFactors = FALSE
)

cv_scenarios

7.2 Predição quando genótipos estão completamente não observados versus mantidos no treinamento

Os dois primeiros esquemas são gerados a partir do mesmo ajuste por folds. Em CV1, o modelo é avaliado em genótipos que foram completamente excluídos do treinamento naquele fold. Em CV2, o modelo é avaliado nos genótipos que permaneceram no treinamento sob esse mesmo ajuste. Portanto, CV2 não é um segundo desenho de mascaramento; ele é a acurácia de referência dentro do fold para os genótipos de treinamento produzida pelo mesmo ajuste usado em CV1. O resumo abaixo deixa essa distinção explícita.

cv12_design <- data.frame(
  Scenario = c("CV1", "CV2"),
  TrainingSet = c(
    "Todas as observações, exceto as dos genótipos retirados",
    "Mesmo ajuste usado em CV1; sem mascaramento adicional"
  ),
  EvaluationTarget = c(
    "Predições para genótipos completamente não observados",
    "Predições para genótipos mantidos no treinamento sob o mesmo fold"
  ),
  stringsAsFactors = FALSE
)

cv12_design

7.3 Predição em ambientes que não estão totalmente disponíveis durante o treinamento

Os esquemas CV0 e CV00 deslocam a dificuldade para o eixo dos ambientes e são derivados do mesmo ajuste com ambiente mascarado dentro de cada fold. Em CV0, o alvo de avaliação é o conjunto de genótipos que já foram vistos em outros ambientes e precisam ser previstos no ambiente retirado. Em CV00, o alvo é mais restritivo: novos genótipos nesse mesmo ambiente retirado. Essa distinção é essencial porque CV0 e CV00 respondem a perguntas de generalização diferentes, embora nasçam do mesmo passo de mascaramento.

cvloo_design <- data.frame(
  Scenario = c("CV0", "CV00"),
  TrainingSet = c(
    "Apenas genótipos de treinamento, com um ambiente totalmente mascarado",
    "Mesmo ajuste usado em CV0; sem mascaramento adicional de ambiente"
  ),
  EvaluationTarget = c(
    "Predições para genótipos vistos em outros ambientes dentro do ambiente retirado",
    "Predições para novos genótipos dentro do ambiente retirado"
  ),
  stringsAsFactors = FALSE
)

cvloo_design

7.4 Nomes de arquivos e reprodutibilidade nas etapas posteriores

O fluxo de predição produz muitos arquivos de saída, e seus nomes precisam carregar informação suficiente para identificar o cenário de validação, o trait, o modelo ajustado, a repetição e o fold ou ambiente deixado de fora. Manter esse padrão explícito é importante porque os módulos seguintes usam esses nomes para inventariar, resumir e visualizar os resultados. A tabela abaixo mostra a convenção de nomes que conecta a etapa de execução aos resumos posteriores.

prediction_file_patterns <- data.frame(
  Scenario = c("CV1", "CV2", "CV0", "CV00"),
  ExampleCSV = c(
    "CV1/GY/rep02/fold03/CV1_GY_Eta1_rep02_fold03.csv",
    "CV2/KW/rep08/fold01/CV2_KW_Eta12_rep08_fold01.csv",
    "CV0/CS11_WS/GY/rep01/fold04/CV0_GY_CS11_WS_Eta10_rep01_fold04.csv",
    "CV00/CS12_WW/KW/rep10/fold02/CV00_KW_CS12_WW_Eta18_rep10_fold02.csv"
  ),
  ExampleBGLR = c(
    "bglr_runs/CV1/GY/rep02/fold03/Eta1/CV1_GY_Eta1_rep02_fold03_GxE_RKHS_varU.dat",
    "bglr_runs/CV2/KW/rep08/fold01/Eta12/CV2_KW_Eta12_rep08_fold01_PxW_RKHS_varU.dat",
    "bglr_runs/CV0/CS11_WS/GY/rep01/fold04/Eta10/CV0_CS11_WS_GY_Eta10_rep01_fold04_GxW_RKHS_varU.dat",
    "bglr_runs/CV00/CS12_WW/KW/rep10/fold02/Eta18/CV00_CS12_WW_KW_Eta18_rep10_fold02_PAKW_RKHS_varU.dat"
  ),
  stringsAsFactors = FALSE
)

prediction_file_patterns

8. Transição para o submódulo de execução

Neste ponto, a etapa de predição já foi organizada a partir de três perspectivas complementares: as entradas analíticas estão definidas, o catálogo reduzido de 18 modelos está explícito e os esquemas de validação estão ligados às perguntas científicas que pretendem responder. Isso significa que a etapa de execução não precisa mais introduzir novos conceitos. Seu papel é aplicar a estrutura definida aqui, gerar os arquivos de predição e organizar as saídas que mais adiante serão resumidas e visualizadas.

O próximo submódulo, analysis_prediction_run_outputs_pt.Rmd, passa então do desenho à implementação. Ali o foco deixa de estar na definição dos modelos e na lógica da validação e passa para os registros de execução, o inventário de arquivos de saída e as tabelas leves que tornam o fluxo de predição mais fácil de inspecionar e reaproveitar nos módulos posteriores.

Navegação entre etapas

Este módulo conecta a definição estrutural da análise preditiva à etapa de execução e saídas que vem em seguida. Leitores que queiram revisar como o conjunto de modelos foi construído devem permanecer neste arquivo. Leitores que queiram inspecionar como as predições são executadas e organizadas devem continuar para o próximo módulo.

Etapa anterior: Componentes de variância
Próxima etapa: Predição — Parte II

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     

loaded via a namespace (and not attached):
 [1] vctrs_0.7.1       cli_3.6.5         knitr_1.50        rlang_1.1.7      
 [5] xfun_0.54         stringi_1.8.7     otel_0.2.0        promises_1.5.0   
 [9] jsonlite_2.0.0    workflowr_1.7.2   glue_1.8.0        rprojroot_2.1.1  
[13] git2r_0.36.2      htmltools_0.5.9   httpuv_1.6.16     sass_0.4.10      
[17] rmarkdown_2.30    evaluate_1.0.5    jquerylib_0.1.4   tibble_3.3.1     
[21] fastmap_1.2.0     yaml_2.3.12       lifecycle_1.0.5   whisker_0.4.1    
[25] stringr_1.6.0     compiler_4.5.1    fs_1.6.6          Rcpp_1.1.1       
[29] pkgconfig_2.0.3   rstudioapi_0.17.1 later_1.4.4       digest_0.6.39    
[33] R6_2.6.1          pillar_1.11.1     magrittr_2.0.4    bslib_0.9.0      
[37] tools_4.5.1       cachem_1.1.0