Resumos e figuras de predição - tutorial didático

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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

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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

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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 arquivo é a versão didática da etapa de visualização. Sua finalidade é tomar as saídas brutas de predição produzidas em analysis_prediction_run_outputs.Rmd, reorganizá-las em tabelas-resumo interpretáveis e depois traduzir esses resumos em comparações compactas e figuras finais.

Neste ponto do pipeline, a principal pergunta analítica já não é mais como cada modelo é especificado, mas como os modelos ajustados se comportam sob diferentes problemas de predição. Por essa razão, a etapa de visualização desloca o foco da construção dos modelos para a interpretação dos modelos. Ela reúne resumos de desempenho para CV1, CV2, CV0 e CV00, destaca contrastes entre modelos com e sem o kernel climático (W) e organiza os resultados em tabelas e figuras que são mais fáceis de inspecionar do que os próprios arquivos brutos de predição.

O fluxo atual de predição grava saídas brutas em output/results/ usando nomes de arquivo que codificam o cenário de validação, o traço, o modelo ajustado, a repetição e o fold. Para CV0 e CV00, o rótulo do ambiente deixado de fora também é registrado no nome do arquivo e depois é associado a Env pelo filtro Env == Env_leave. Essa estrutura de nomes é essencial porque conecta a etapa de execução à etapa de interpretação.

Ao longo deste tutorial, o kernel climático (W) deve ser interpretado como uma fonte de similaridade derivada do clima que complementa, mas não substitui, o efeito categórico de ambiente (E). O componente fenômico (P) também deve ser interpretado com cuidado, porque a similaridade baseada em NIRS pode refletir forte resposta ambiental além de sinal preditivo potencialmente útil.

O que este script faz

O fluxo é organizado em quatro tarefas práticas:

1. localizar e ler os arquivos CSV de predição;
2. interpretar os nomes dos arquivos e recuperar as informações de traço, modelo, repetição e leave-one-environment-out;
3. resumir os resultados para CV1, CV2, CV0 e CV00;
4. exportar tabelas limpas e figuras para a próxima etapa orientada ao manuscrito.

Para tornar a interpretação mais fácil, os modelos são ordenados para ajudar a responder três perguntas práticas:

1. passar de kernels lineares para kernels Gaussianos ou arc-cosine melhora a capacidade preditiva?
2. adicionar o kernel climático (W) melhora a capacidade preditiva?
3. combinar informação genômica e fenômica melhora a capacidade preditiva?

Entradas, saídas e próxima etapa

Entradas desta etapa - saídas brutas de predição gravadas por analysis_prediction_run_outputs.Rmd - tabelas leves de metadados da predição geradas na etapa anterior

Saídas desta etapa - tabelas-resumo finais - rankings de modelos - comparações do efeito de W - comparações entre famílias de kernel - figuras finais para interpretação posterior

Próxima etapa - as saídas geradas aqui alimentam os resumos orientados ao manuscrito e a interpretação final dos resultados do projeto.

1. Configuração

O documento começa definindo um estilo consistente de renderização para código, tabelas e figuras. Isso ajuda o tutorial a permanecer legível quando é inspecionado em HTML e mantém estável a apresentação visual entre os diferentes outputs produzidos nesta etapa.

Este chunk inicia uma sessão limpa e define a raiz do projeto usada pelo módulo de visualização. O objetivo é tornar reprodutível desde o início a leitura e o resumo das saídas de predição.

knitr::opts_chunk$set( echo = TRUE,
 warning = FALSE,
 message = FALSE,
 fig.width = 13,
 fig.height = 8,
  out.width = "100%" )

2. Carregar pacotes e localizar as pastas do projeto

A etapa de visualização depende de um pequeno conjunto de pacotes para manipulação de dados, construção de figuras e formatação de tabelas. Ela também precisa localizar corretamente as pastas do projeto, porque os resumos gerados aqui devem ser gravados em caminhos de saída estáveis que depois podem ser reutilizados pelo site e por materiais orientados ao manuscrito.

Esta seção padroniza a raiz do projeto, localiza as pastas que contêm as saídas de predição e prepara os diretórios nos quais as tabelas e figuras finais serão gravadas.

Este chunk carrega os pacotes necessários para leitura, resumo e visualização dos resultados de predição. Ele também define as pastas onde ficam as saídas brutas e as tabelas derivadas.

rm(list = ls())
options(scipen = 999)
packages_needed <- c( "dplyr",
 "tidyr",
 "readr",
 "tibble",
 "ggplot2",
 "ggthemes",
 "knitr",
 "kableExtra",
  "stringr" )
packages_missing <- packages_needed[!(packages_needed %in% installed.packages()[, "Package"])]
if (length(packages_missing) > 0) {
  stop(
    paste0(
      "Os seguintes pacotes são necessários para este módulo, mas não estão instalados: ",
      paste(packages_missing, collapse = ", "),
      ". Instale-os antes de renderizar visualization_pt.Rmd."
    )
  )
}
library(dplyr)
library(tidyr)
library(readr)
library(tibble)
library(ggplot2)
library(ggthemes)
library(knitr)
library(kableExtra)
library(stringr)
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 localizar a pasta 'output/'.\nExecute este script a partir da raiz do projeto ou de analysis/.")
}
results_dir <- file.path(project_root, "output", "results")
figures_dir <- file.path(project_root, "output", "figures")
tables_dir <- file.path(project_root, "output", "tables")
dir.create(figures_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(tables_dir, recursive = TRUE, showWarnings = FALSE)

3. Catálogo de modelos usado no pipeline reduzido

A etapa de visualização usa o mesmo catálogo reduzido de 18 modelos adotado no fluxo de predição. Aqui esse catálogo é reorganizado para que os resumos se tornem mais fáceis de interpretar em termos analíticos. Em vez de mostrar apenas números de modelo, a tabela enfatiza a via representada, a família de kernel utilizada e se a informação climática está presente.

Essa organização é importante porque as comparações mostradas mais adiante no documento não são arbitrárias. Elas foram desenhadas para ajudar o leitor a entender de onde vêm os ganhos preditivos e se esses ganhos estão associados à informação climática, à escolha do kernel ou à integração entre fontes genômicas e fenômicas.

Este chunk recria um catálogo compacto de modelos para que os resumos e figuras possam ser interpretados com rótulos estáveis. Manter o catálogo próximo da etapa de visualização facilita acompanhar as comparações.

model_catalog <- tibble( ModelNumber = c(1, 10, 3, 13, 5, 16, 2, 11, 4, 14, 6, 17, 7, 12, 8, 15, 9, 18),
 Model = paste0("Eta", c(1, 10, 3, 13, 5, 16, 2, 11, 4, 14, 6, 17, 7, 12, 8, 15, 9, 18)),
 Pathway = c( rep("Via genômica", 6), rep("Via fenômica", 6), rep("Via combinada G + P", 6) ),
 KernelFamily = c( "Linear", "Linear", "Gaussiano", "Gaussiano", "Arc-cosine", "Arc-cosine", "Linear", "Linear", "Gaussiano", "Gaussiano", "Arc-cosine", "Arc-cosine", "Linear", "Linear", "Gaussiano", "Gaussiano", "Arc-cosine", "Arc-cosine" ),
 WeatherStatus = c( "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W", "Sem W", "Com W" ),
 Group = c( "Base genômica", "Clima fonte única", "Base genômica", "Clima fonte única", "Base genômica", "Clima fonte única", "Base fenômica", "Clima fonte única", "Base fenômica", "Clima fonte única", "Base fenômica", "Clima fonte única", "Base combinada", "Clima combinado", "Base combinada", "Clima combinado", "Base combinada", "Clima combinado" ),
 Description = c( "Genômico linear + E", "Genômico linear + W", "Genômico Gaussiano + E", "Genômico Gaussiano + W", "Genômico arc-cosine + E", "Genômico arc-cosine + W", "Fenômico linear + E", "Fenômico linear + W", "Fenômico Gaussiano + E", "Fenômico Gaussiano + W", "Fenômico arc-cosine + E", "Fenômico arc-cosine + W", "Combinado linear + E", "Combinado linear + W", "Combinado Gaussiano + E", "Combinado Gaussiano + W", "Combinado arc-cosine + E", "Combinado arc-cosine + W" ),
  ShortDescription = c( "G + E", "G + W", "GGK + E", "GGK + W", "GAK + E", "GAK + W", "P + E", "P + W", "PGK + E", "PGK + W", "PAK + E", "PAK + W", "G+P + E", "G+P + W", "GGK+PGK + E", "GGK+PGK + W", "GAK+PAK + E", "GAK+PAK + W" ) )
model_catalog

4. Pares de comparação usados para contrastes entre com W e sem W

Uma pergunta central neste projeto é se adicionar o kernel climático (W) melhora a capacidade preditiva. Para responder a essa pergunta de forma justa, a comparação deve sempre ser feita entre modelos estruturalmente semelhantes e que diferem apenas pela inclusão da informação climática.

Os pares de comparação definidos abaixo fornecem exatamente essa estrutura. Eles são usados mais adiante para construir tabelas que quantificam quanto o desempenho preditivo muda quando W é introduzido dentro da mesma via e da mesma família de kernel.

Este chunk define os pares de modelos e as famílias que serão contrastados nas tabelas-resumo. Esses agrupamentos fornecem a estrutura analítica usada para interpretar o desempenho preditivo.

comparison_pairs <- tibble( Comparison = c( "Genômico linear", "Genômico Gaussiano", "Genômico arc-cosine", "Fenômico linear", "Fenômico Gaussiano", "Fenômico arc-cosine", "Combinado linear", "Combinado Gaussiano", "Combinado arc-cosine" ),
 WithoutW = c("Eta1", "Eta3", "Eta5", "Eta2", "Eta4", "Eta6", "Eta7", "Eta8", "Eta9"),
  WithW = c("Eta10", "Eta13", "Eta16", "Eta11", "Eta14", "Eta17", "Eta12", "Eta15", "Eta18") )
comparison_pairs

5. Localizar arquivos de predição

O primeiro passo operacional da etapa de visualização é localizar os arquivos brutos de predição produzidos pelo módulo anterior. No padrão atual do pipeline, esses arquivos são gravados recursivamente em output/results/, com uma hierarquia explícita por esquema de validação, traço, repetição e fold.

Nesta etapa, o script lê a pasta de resultados de forma recursiva, mantém apenas os CSVs de predição armazenados dentro dos ramos estruturados CV1, CV2, CV0 e CV00, e ignora outros artefatos, como bglr_runs. Isso torna os resumos posteriores compatíveis com o novo contrato Rep/Fold e preserva a separação entre os problemas de validação baseados em genótipos e os problemas leave-one-environment-out.

Este chunk restaura as tabelas e objetos de visualização já gerados para que a versão em português possa enfatizar a apresentação e a interpretação dos resultados finais. Assim, o documento permanece alinhado ao fluxo principal sem repetir toda a leitura das saídas brutas.

Este chunk varre as pastas de resultados e registra os arquivos CSV disponíveis para a etapa de visualização. O inventário resultante define exatamente quais saídas de predição serão resumidas.

all_result_rel_paths <- list.files(
  results_dir,
  pattern = "\\.csv$",
  full.names = FALSE,
  recursive = TRUE
)

all_result_rel_paths <- gsub("\\\\", "/", all_result_rel_paths)

structured_idx <- grepl("^CV1/|^CV2/|^CV0/|^CV00/", all_result_rel_paths)
all_result_rel_paths <- all_result_rel_paths[structured_idx]
all_result_files <- file.path(results_dir, all_result_rel_paths)

cv12_idx <- grepl("^CV1/|^CV2/", all_result_rel_paths)
cvloo_idx <- grepl("^CV0/|^CV00/", all_result_rel_paths)

cv12_files <- all_result_files[cv12_idx]
cv12_rel_paths <- all_result_rel_paths[cv12_idx]

cvloo_files <- all_result_files[cvloo_idx]
cvloo_rel_paths <- all_result_rel_paths[cvloo_idx]

cat("Número de arquivos CV1/CV2:", length(cv12_files), "\n")
cat("Número de arquivos CV0/CV00:", length(cvloo_files), "\n")

if (length(cv12_files) == 0 && length(cvloo_files) == 0) {
  stop(
    "Nenhum arquivo CSV de predição foi encontrado em output/results/. ",
    "Execute analysis_prediction_run_outputs primeiro."
  )
}

6. Ler os arquivos CV1 e CV2 gerados pelo script final de predição

Os arquivos CV1 e CV2 contêm as saídas brutas necessárias para resumir o desempenho preditivo quando o desafio é definido no eixo dos genótipos. No fluxo atual, repetição e fold ficam codificados tanto na hierarquia de pastas quanto no nome do arquivo, então o script reconstrói esses metadados explicitamente antes de montar os resumos.

Esse desenho é útil porque mantém o sistema de arquivos organizado para inspeção rápida e, ao mesmo tempo, permite que a etapa de visualização reconstrua tabelas unificadas ao longo de repetições e folds.

Este chunk lê os arquivos de resultados de CV1 e CV2 e converte seus caminhos em identificadores estruturados, como trait, repetição, fold e modelo. Essa transformação é essencial para as agregações posteriores.

cv12_list <- vector("list", length(cv12_files))

for (i in seq_along(cv12_files)) {
  current_file <- cv12_files[i]
  current_rel_path <- cv12_rel_paths[i]
  path_parts <- strsplit(current_rel_path, "/")[[1]]

  if (length(path_parts) != 5) {
    stop(
      "Estrutura de diretórios inesperada para arquivo CV1/CV2: ",
      current_rel_path,
      ". Padrão esperado: CV1/<trait>/repXX/foldXX/<file>.csv ou ",
      "CV2/<trait>/repXX/foldXX/<file>.csv."
    )
  }

  current_cv <- path_parts[1]
  trait_from_path <- path_parts[2]
  rep_from_path <- as.numeric(gsub("^rep", "", path_parts[3]))
  fold_from_path <- as.numeric(gsub("^fold", "", path_parts[4]))

  file_name <- sub("\\.csv$", "", basename(current_file))
  file_parts <- strsplit(file_name, "_")[[1]]

  if (length(file_parts) != 5) {
    stop(
      "Nome de arquivo inesperado para CV1/CV2: ",
      file_name,
      ". Padrão esperado: CV1_<trait>_Eta<model>_repXX_foldXX.csv ou ",
      "CV2_<trait>_Eta<model>_repXX_foldXX.csv."
    )
  }

  current_trait <- file_parts[2]
  current_model_label <- file_parts[3]
  current_model_number <- as.numeric(gsub("^Eta", "", current_model_label))
  current_rep <- as.numeric(gsub("^rep", "", file_parts[4]))
  current_fold <- as.numeric(gsub("^fold", "", file_parts[5]))

  if (
    !identical(current_cv, file_parts[1]) ||
    !identical(trait_from_path, current_trait) ||
    !identical(rep_from_path, current_rep) ||
    !identical(fold_from_path, current_fold)
  ) {
    stop("Inconsistência entre metadados do caminho e do nome do arquivo em: ", current_rel_path)
  }

  current_data <- readr::read_csv(current_file, show_col_types = FALSE)

  if (!("Env" %in% names(current_data)) || !("cor" %in% names(current_data))) {
    stop(
      "Estrutura de colunas inesperada no arquivo: ",
      file_name,
      ". O script final de predição deve exportar colunas incluindo Env e cor."
    )
  }

  current_data <- current_data %>%
    mutate(
      RelativePath = current_rel_path,
      Fold = current_fold,
      CV = current_cv,
      Trait = current_trait,
      ModelNumber = current_model_number,
      Model = current_model_label,
      Rep = current_rep
    )

  cv12_list[[i]] <- current_data
}

cv12_raw <- bind_rows(cv12_list) %>%
  left_join(model_catalog, by = c("ModelNumber", "Model"))

if (!("Runtime_sec" %in% names(cv12_raw))) {
  cv12_raw$Runtime_sec <- NA_real_
}

readr::write_csv(cv12_raw, file.path(tables_dir, "prediction_cv1_cv2_raw.csv"))

7. Ler os arquivos CV0 e CV00 gerados pelo script final de predição

Os arquivos CV0 e CV00 representam os problemas de predição mais exigentes deste estudo porque envolvem um ambiente retirado do ajuste. No padrão atual do pipeline, o ambiente leave-one-environment-out é codificado tanto na hierarquia de pastas quanto no nome do arquivo, então o script reconstrói essa informação explicitamente antes de montar os resumos leave-one-environment-out.

Esta seção lê esses arquivos, verifica a consistência dos caminhos estruturados e prepara os metadados necessários para manter Env_leave explícito ao longo dos resumos.

Este chunk lê os arquivos de resultados de CV0 e CV00 e extrai os identificadores sensíveis ao ambiente guardados nos caminhos e nomes dos arquivos. O resultado fornece a tabela em formato longo usada nos resumos leave-one-environment-out.

cvloo_list <- vector("list", length(cvloo_files))

for (i in seq_along(cvloo_files)) {
  current_file <- cvloo_files[i]
  current_rel_path <- cvloo_rel_paths[i]
  path_parts <- strsplit(current_rel_path, "/")[[1]]

  if (length(path_parts) != 6) {
    stop(
      "Estrutura de diretórios inesperada para arquivo CV0/CV00: ",
      current_rel_path,
      ". Padrão esperado: CV0/<Env_leave>/<trait>/repXX/foldXX/<file>.csv ou ",
      "CV00/<Env_leave>/<trait>/repXX/foldXX/<file>.csv."
    )
  }

  current_cv <- path_parts[1]
  env_from_path <- path_parts[2]
  trait_from_path <- path_parts[3]
  rep_from_path <- as.numeric(gsub("^rep", "", path_parts[4]))
  fold_from_path <- as.numeric(gsub("^fold", "", path_parts[5]))

  file_name <- sub("\\.csv$", "", basename(current_file))
  file_parts <- strsplit(file_name, "_")[[1]]

  if (length(file_parts) < 6) {
    stop(
      "Nome de arquivo inesperado para CV0/CV00: ",
      file_name,
      ". Padrão esperado: CV0_<trait>_<Env_leave>_Eta<model>_repXX_foldXX.csv ou ",
      "CV00_<trait>_<Env_leave>_Eta<model>_repXX_foldXX.csv."
    )
  }

  current_trait <- file_parts[2]
  current_model_label <- file_parts[length(file_parts) - 2]
  current_model_number <- as.numeric(gsub("^Eta", "", current_model_label))
  current_rep <- as.numeric(gsub("^rep", "", file_parts[length(file_parts) - 1]))
  current_fold <- as.numeric(gsub("^fold", "", file_parts[length(file_parts)]))
  current_env_leave <- paste(file_parts[3:(length(file_parts) - 3)], collapse = "_")

  if (
    !identical(current_cv, file_parts[1]) ||
    !identical(env_from_path, current_env_leave) ||
    !identical(trait_from_path, current_trait) ||
    !identical(rep_from_path, current_rep) ||
    !identical(fold_from_path, current_fold)
  ) {
    stop("Inconsistência entre metadados do caminho e do nome do arquivo em: ", current_rel_path)
  }

  current_data <- readr::read_csv(current_file, show_col_types = FALSE)

  if (!("Env" %in% names(current_data)) || !("cor" %in% names(current_data))) {
    stop(
      "Estrutura de colunas inesperada no arquivo: ",
      file_name,
      ". O script final de predição deve exportar colunas incluindo Env e cor."
    )
  }

  current_data <- current_data %>%
    mutate(
      RelativePath = current_rel_path,
      Fold = current_fold,
      CV = current_cv,
      Trait = current_trait,
      Env_leave = current_env_leave,
      ModelNumber = current_model_number,
      Model = current_model_label,
      Rep = current_rep
    )

  cvloo_list[[i]] <- current_data
}

cvloo_raw <- bind_rows(cvloo_list) %>%
  left_join(model_catalog, by = c("ModelNumber", "Model"))

if (!("Runtime_sec" %in% names(cvloo_raw))) {
  cvloo_raw$Runtime_sec <- NA_real_
}

readr::write_csv(cvloo_raw, file.path(tables_dir, "prediction_cv0_cv00_raw.csv"))
readr::write_csv(model_catalog, file.path(tables_dir, "prediction_model_catalog.csv"))

8. Resumir CV1 e CV2

Esta etapa constrói os resumos para CV1 e CV2 de forma sequencial: primeiro por ambiente e fold, depois por ambiente dentro da repetição, depois por repetição e finalmente por médias e desvios-padrão em nível de modelo ao longo das repetições.

Este chunk agrega os resultados de CV1 e CV2 em resumos por modelo. Essas tabelas quantificam o comportamento preditivo médio ao longo de repetições e folds nos cenários de partição genotípica.

cv12_env_fold <- cv12_raw %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
   CV,
   Rep,
   Fold,
    Env ) %>%
  summarise( EnvCorrelation = mean(cor, na.rm = TRUE),
   ValidationCount = sum(!is.na(cor)),
   MeanRuntimeSec = mean(Runtime_sec, na.rm = TRUE),
    .groups = "drop" ) %>%
  mutate( EnvCorrelation = ifelse(is.nan(EnvCorrelation), NA_real_, EnvCorrelation),
    MeanRuntimeSec = ifelse(is.nan(MeanRuntimeSec), NA_real_, MeanRuntimeSec) ) %>%
  filter(!is.na(EnvCorrelation), ValidationCount > 0)
cv12_env_rep <- cv12_env_fold %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
   CV,
   Rep,
    Env ) %>%
  summarise( EnvCorrelation = weighted.mean(EnvCorrelation, w = ValidationCount, na.rm = TRUE),
   ValidationCount = sum(ValidationCount, na.rm = TRUE),
   MeanRuntimeSec = mean(MeanRuntimeSec, na.rm = TRUE),
    .groups = "drop" )
cv12_rep_summary <- cv12_env_rep %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
   CV,
    Rep ) %>%
  summarise( WeightedCorrelation = weighted.mean(EnvCorrelation, w = ValidationCount, na.rm = TRUE),
   MeanRuntimeSec = mean(MeanRuntimeSec, na.rm = TRUE),
    .groups = "drop" )
cv12_summary <- cv12_rep_summary %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
    CV ) %>%
  summarise( MeanCorrelation = mean(WeightedCorrelation, na.rm = TRUE),
   SDCorrelation = sd(WeightedCorrelation, na.rm = TRUE),
   NumberOfRepetitions = n(),
   MeanRuntimeSec = mean(MeanRuntimeSec, na.rm = TRUE),
    .groups = "drop" ) %>%
  mutate( SDCorrelation = ifelse(is.na(SDCorrelation), 0, SDCorrelation) )
readr::write_csv(cv12_env_fold, file.path(tables_dir, "prediction_cv1_cv2_by_rep_fold_env.csv"))
readr::write_csv(cv12_env_rep, file.path(tables_dir, "prediction_cv1_cv2_by_rep_env.csv"))
readr::write_csv(cv12_rep_summary, file.path(tables_dir, "prediction_cv1_cv2_by_rep.csv"))
readr::write_csv(cv12_summary, file.path(tables_dir, "prediction_cv1_cv2_summary.csv"))

9. Resumir CV0 e CV00

Para cenários leave-one-environment-out, o script primeiro restringe os resumos às linhas em que Env == Env_leave. Isso mantém apenas o componente do ambiente deixado de fora que é conceitualmente relevante para CV0 e CV00.

Este chunk agrega os resultados de CV0 e CV00 em resumos sensíveis ao ambiente. O objetivo é descrever o comportamento da predição quando o problema de validação se centra na extrapolação entre ambientes.

cvloo_env_filtered <- cvloo_raw %>%
  filter(Env == Env_leave)
cvloo_env_fold <- cvloo_env_filtered %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
   CV,
   Env_leave,
   Rep,
   Fold,
    Env ) %>%
  summarise( LOEOCorrelation = mean(cor, na.rm = TRUE),
   ValidationCount = sum(!is.na(cor)),
   MeanRuntimeSec = mean(Runtime_sec, na.rm = TRUE),
    .groups = "drop" ) %>%
  mutate( LOEOCorrelation = ifelse(is.nan(LOEOCorrelation), NA_real_, LOEOCorrelation),
    MeanRuntimeSec = ifelse(is.nan(MeanRuntimeSec), NA_real_, MeanRuntimeSec) ) %>%
  filter(!is.na(LOEOCorrelation), ValidationCount > 0)
cvloo_rep_summary <- cvloo_env_fold %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
   CV,
   Env_leave,
    Rep ) %>%
  summarise( LOEOCorrelation = weighted.mean(LOEOCorrelation, w = ValidationCount, na.rm = TRUE),
   ValidationCount = sum(ValidationCount, na.rm = TRUE),
   MeanRuntimeSec = mean(MeanRuntimeSec, na.rm = TRUE),
    .groups = "drop" )
cvloo_summary <- cvloo_rep_summary %>%
  group_by( ModelNumber,
   Model,
   Group,
   Pathway,
   KernelFamily,
   WeatherStatus,
   Description,
   ShortDescription,
   Trait,
   CV,
    Env_leave ) %>%
  summarise( MeanCorrelation = mean(LOEOCorrelation, na.rm = TRUE),
   SDCorrelation = sd(LOEOCorrelation, na.rm = TRUE),
   NumberOfRuns = n(),
   MeanRuntimeSec = mean(MeanRuntimeSec, na.rm = TRUE),
    .groups = "drop" ) %>%
  mutate( SDCorrelation = ifelse(is.na(SDCorrelation), 0, SDCorrelation) )
readr::write_csv(cvloo_env_fold, file.path(tables_dir, "prediction_cv0_cv00_by_rep_fold_env.csv"))
readr::write_csv(cvloo_rep_summary, file.path(tables_dir, "prediction_cv0_cv00_by_run.csv"))
readr::write_csv(cvloo_summary, file.path(tables_dir, "prediction_cv0_cv00_summary.csv"))

10. Tabelas de comparação

Esta seção deriva tabelas analíticas secundárias, especialmente o contraste entre modelos com e sem W e a comparação entre famílias de kernel dentro de cada via.

Este chunk constrói as tabelas de comparação derivadas que sustentam o ranking e a interpretação dos modelos. Essas tabelas traduzem os resumos brutos em contrastes analíticos mais focados.

cv12_w_effect <- comparison_pairs %>%
  left_join( cv12_summary %>% select(Trait, CV, Model, MeanCorrelation, SDCorrelation),
    by = c("WithoutW" = "Model") ) %>%
  rename( MeanWithoutW = MeanCorrelation, SDWithoutW = SDCorrelation ) %>%
  left_join( cv12_summary %>% select(Trait, CV, Model, MeanCorrelation, SDCorrelation),
    by = c("WithW" = "Model", "Trait", "CV") ) %>%
  rename( MeanWithW = MeanCorrelation, SDWithW = SDCorrelation ) %>%
  mutate( DeltaWithW = MeanWithW - MeanWithoutW ) %>%
  arrange(Trait, CV, Comparison)
cvloo_w_effect <- comparison_pairs %>%
  left_join( cvloo_summary %>% select(Trait, CV, Env_leave, Model, MeanCorrelation, SDCorrelation),
    by = c("WithoutW" = "Model") ) %>%
  rename( MeanWithoutW = MeanCorrelation, SDWithoutW = SDCorrelation ) %>%
  left_join( cvloo_summary %>% select(Trait, CV, Env_leave, Model, MeanCorrelation, SDCorrelation),
    by = c("WithW" = "Model", "Trait", "CV", "Env_leave") ) %>%
  rename( MeanWithW = MeanCorrelation, SDWithW = SDCorrelation ) %>%
  mutate( DeltaWithW = MeanWithW - MeanWithoutW ) %>%
  arrange(Trait, CV, Env_leave, Comparison)
cv12_kernel_family_compare <- cv12_summary %>%
  select(Trait, CV, Pathway, WeatherStatus, KernelFamily, MeanCorrelation) %>%
  pivot_wider(names_from = KernelFamily, values_from = MeanCorrelation) %>%
  mutate( Delta_Gaussian_vs_Linear = Gaussian - Linear,
   Delta_ArcCos_vs_Linear = `Arc-cosine` - Linear,
    Delta_ArcCos_vs_Gaussian = `Arc-cosine` - Gaussian ) %>%
  arrange(Trait, CV, Pathway, WeatherStatus)
cvloo_kernel_family_compare <- cvloo_summary %>%
  select(Trait, CV, Env_leave, Pathway, WeatherStatus, KernelFamily, MeanCorrelation) %>%
  pivot_wider(names_from = KernelFamily, values_from = MeanCorrelation) %>%
  mutate( Delta_Gaussian_vs_Linear = Gaussian - Linear,
   Delta_ArcCos_vs_Linear = `Arc-cosine` - Linear,
    Delta_ArcCos_vs_Gaussian = `Arc-cosine` - Gaussian ) %>%
  arrange(Trait, CV, Env_leave, Pathway, WeatherStatus)
readr::write_csv(cv12_w_effect, file.path(tables_dir, "prediction_cv1_cv2_w_effect_pairs.csv"))
readr::write_csv(cvloo_w_effect, file.path(tables_dir, "prediction_cv0_cv00_w_effect_pairs.csv"))
readr::write_csv(cv12_kernel_family_compare,
  file.path(tables_dir, "prediction_cv1_cv2_kernel_family_compare.csv"))
readr::write_csv(cvloo_kernel_family_compare,
  file.path(tables_dir, "prediction_cv0_cv00_kernel_family_compare.csv"))

11. Tabelas compactas de display

Esses objetos são versões simplificadas dos resumos completos. Eles são úteis para tabelas do manuscrito, material suplementar e inspeção rápida dos modelos com melhor desempenho.

Este chunk formata e salva as principais tabelas usadas posteriormente no relatório. Ele oferece uma apresentação legível dos resumos numéricos antes da construção das figuras.

cv12_table_display <- cv12_summary %>%
  arrange(Trait, CV, Pathway, KernelFamily, WeatherStatus) %>%
  mutate( MeanCorrelation = round(MeanCorrelation, 3),
   SDCorrelation = round(SDCorrelation, 3),
    MeanRuntimeSec = round(MeanRuntimeSec, 2) ) %>%
  select( Trait,
   CV,
   Pathway,
   KernelFamily,
   WeatherStatus,
   ShortDescription,
   MeanCorrelation,
   SDCorrelation,
   NumberOfRepetitions,
    MeanRuntimeSec )
cvloo_table_display <- cvloo_summary %>%
  arrange(Trait, CV, Env_leave, Pathway, KernelFamily, WeatherStatus) %>%
  mutate( MeanCorrelation = round(MeanCorrelation, 3),
   SDCorrelation = round(SDCorrelation, 3),
    MeanRuntimeSec = round(MeanRuntimeSec, 2) ) %>%
  select( Trait,
   CV,
   Env_leave,
   Pathway,
   KernelFamily,
   WeatherStatus,
   ShortDescription,
   MeanCorrelation,
   SDCorrelation,
   NumberOfRuns,
    MeanRuntimeSec )
cv12_top3 <- cv12_summary %>%
  group_by(Trait, CV) %>%
  slice_max(order_by = MeanCorrelation, n = 3, with_ties = FALSE) %>%
  ungroup() %>%
  mutate( MeanCorrelation = round(MeanCorrelation, 3), SDCorrelation = round(SDCorrelation, 3) ) %>%
  select(Trait, CV, ShortDescription, MeanCorrelation, SDCorrelation)
cvloo_top3 <- cvloo_summary %>%
  group_by(Trait, CV, Env_leave) %>%
  slice_max(order_by = MeanCorrelation, n = 3, with_ties = FALSE) %>%
  ungroup() %>%
  mutate( MeanCorrelation = round(MeanCorrelation, 3), SDCorrelation = round(SDCorrelation, 3) ) %>%
  select(Trait, CV, Env_leave, ShortDescription, MeanCorrelation, SDCorrelation)
readr::write_csv(cv12_table_display, file.path(tables_dir, "prediction_cv1_cv2_display_table.csv"))
readr::write_csv(cvloo_table_display, file.path(tables_dir, "prediction_cv0_cv00_display_table.csv"))
readr::write_csv(cv12_top3, file.path(tables_dir, "prediction_cv1_cv2_top3.csv"))
readr::write_csv(cvloo_top3, file.path(tables_dir, "prediction_cv0_cv00_top3.csv"))

12. Tabelas Top-3

As duas tabelas seguintes exibem os três modelos com melhor desempenho em cada esquema de validação.

Este chunk apresenta os modelos de melhor desempenho nos cenários CV1 e CV2. O objetivo é destacar os principais candidatos sob validação por partição de genótipos.

cv12_top3 %>%
  knitr::kable( caption = "Top 3 modelos para cada traço e esquema de validação em CV1 e CV2.",
    align = "llccc" ) %>%
  kableExtra::kable_styling( bootstrap_options = c("striped", "hover", "condensed", "responsive"),
   full_width = FALSE,
    position = "left" )

Top 3 modelos para cada traço e esquema de validação em CV1 e CV2.

Trait	CV	ShortDescription	MeanCorrelation	SDCorrelation
GY	CV1	GGK + W	0.727	0.012
GY	CV1	G + W	0.725	0.011
GY	CV1	GGK+PGK + W	0.723	0.011
GY	CV2	GGK+PGK + W	0.991	0.000
GY	CV2	GGK+PGK + E	0.989	0.000
GY	CV2	G + W	0.984	0.000
KW	CV1	P + E	0.762	0.010
KW	CV1	P + W	0.761	0.005
KW	CV1	G+P + E	0.728	0.015
KW	CV2	G+P + W	0.994	0.001
KW	CV2	GGK+PGK + W	0.994	0.000
KW	CV2	G+P + E	0.994	0.000

Este chunk apresenta os modelos de melhor desempenho nos cenários CV0 e CV00. O foco é destacar os melhores desempenhos sob validação leave-one-environment-out.

cvloo_top3 %>%
  knitr::kable( caption = "Top 3 modelos para cada traço, esquema de validação e ambiente deixado de fora em CV0 e CV00.",
    align = "lllccc" ) %>%
  kableExtra::kable_styling( bootstrap_options = c("striped", "hover", "condensed", "responsive"),
   full_width = FALSE,
    position = "left" )

Top 3 modelos para cada traço, esquema de validação e ambiente deixado de fora em CV0 e CV00.

Trait	CV	Env_leave	ShortDescription	MeanCorrelation	SDCorrelation
GY	CV0	CS11_WS	GAK + E	0.926	0.000
GY	CV0	CS11_WS	GAK + W	0.924	0.003
GY	CV0	CS11_WS	GGK + E	0.923	0.000
GY	CV0	CS11_WW	GGK + W	0.896	0.000
GY	CV0	CS11_WW	G + W	0.896	0.000
GY	CV0	CS11_WW	GGK + E	0.894	0.001
GY	CV0	CS12_WS	GGK + E	0.896	0.001
GY	CV0	CS12_WS	G + E	0.894	0.001
GY	CV0	CS12_WS	GGK + W	0.893	0.001
GY	CV0	CS12_WW	G + W	0.909	0.000
GY	CV0	CS12_WW	GGK + W	0.907	0.000
GY	CV0	CS12_WW	G + E	0.904	0.001
GY	CV00	CS11_WS	GAK + W	0.735	0.014
GY	CV00	CS11_WS	GAK + E	0.732	0.013
GY	CV00	CS11_WS	G + W	0.720	0.016
GY	CV00	CS11_WW	G + E	0.741	0.010
GY	CV00	CS11_WW	GGK + W	0.740	0.013
GY	CV00	CS11_WW	G + W	0.738	0.012
GY	CV00	CS12_WS	GGK + W	0.734	0.007
GY	CV00	CS12_WS	G + W	0.733	0.007
GY	CV00	CS12_WS	GGK + E	0.730	0.011
GY	CV00	CS12_WW	GGK+PGK + W	0.714	0.013
GY	CV00	CS12_WW	GGK + W	0.713	0.009
GY	CV00	CS12_WW	G + W	0.710	0.008
KW	CV0	CS11_WS	GAK + E	0.961	0.000
KW	CV0	CS11_WS	GGK + E	0.960	0.000
KW	CV0	CS11_WS	G + E	0.960	0.000
KW	CV0	CS11_WW	G + W	0.889	0.000
KW	CV0	CS11_WW	GGK+PGK + W	0.888	0.001
KW	CV0	CS11_WW	GGK + W	0.887	0.001
KW	CV0	CS12_WS	GGK + W	0.899	0.001
KW	CV0	CS12_WS	GGK + E	0.898	0.001
KW	CV0	CS12_WS	G + W	0.897	0.001
KW	CV0	CS12_WW	GGK + W	0.949	0.000
KW	CV0	CS12_WW	GGK+PGK + W	0.948	0.000
KW	CV0	CS12_WW	G + W	0.948	0.000
KW	CV00	CS11_WS	P + E	0.662	0.074
KW	CV00	CS11_WS	G+P + E	0.654	0.036
KW	CV00	CS11_WS	P + W	0.624	0.068
KW	CV00	CS11_WW	P + E	0.656	0.054
KW	CV00	CS11_WW	GGK + W	0.567	0.023
KW	CV00	CS11_WW	GGK+PGK + E	0.566	0.021
KW	CV00	CS12_WS	GGK+PGK + E	0.619	0.013
KW	CV00	CS12_WS	GGK + E	0.616	0.017
KW	CV00	CS12_WS	G + W	0.613	0.009
KW	CV00	CS12_WW	P + E	0.640	0.061
KW	CV00	CS12_WW	G+P + E	0.636	0.041
KW	CV00	CS12_WW	GGK+PGK + E	0.617	0.010

13. Tabelas do efeito de W

Essas tabelas apresentam as comparações pareadas entre modelos sem e com informação climática.

Este chunk resume como a inclusão do efeito climático altera o desempenho em CV1 e CV2. A comparação ajuda a interpretar a contribuição das extensões de modelo informadas por clima.

cv12_w_effect %>%
  mutate( MeanWithoutW = round(MeanWithoutW, 3),
   MeanWithW = round(MeanWithW, 3),
    DeltaWithW = round(DeltaWithW, 3) ) %>%
  knitr::kable( caption = "Comparação pareada entre modelos sem e com o kernel climático (W) em CV1 e CV2.",
    align = "lllccccc" ) %>%
  kableExtra::kable_styling( bootstrap_options = c("striped", "hover", "condensed", "responsive"),
   full_width = FALSE,
    position = "left" ) %>%
  kableExtra::scroll_box(width = "100%", height = "320px")

Comparação pareada entre modelos sem e com o kernel climático (W) em CV1 e CV2.

Comparison	WithoutW	WithW	Trait	CV	MeanWithoutW	SDWithoutW	MeanWithW	SDWithW	DeltaWithW
Combinado Gaussiano	Eta8	Eta15	GY	CV1	0.717	0.0117158	0.723	0.0112130	0.006
Combinado arc-cosine	Eta9	Eta18	GY	CV1	0.703	0.0100091	0.706	0.0087728	0.003
Combinado linear	Eta7	Eta12	GY	CV1	0.713	0.0134212	0.719	0.0124435	0.006
Genômico Gaussiano	Eta3	Eta13	GY	CV1	0.719	0.0106337	0.727	0.0120687	0.008
Genômico arc-cosine	Eta5	Eta16	GY	CV1	0.711	0.0065960	0.714	0.0083470	0.002
Genômico linear	Eta1	Eta10	GY	CV1	0.719	0.0128201	0.725	0.0109925	0.005
Fenômico Gaussiano	Eta4	Eta14	GY	CV1	0.134	0.0274657	0.147	0.0191283	0.013
Fenômico arc-cosine	Eta6	Eta17	GY	CV1	0.178	0.0296188	0.177	0.0380569	-0.001
Fenômico linear	Eta2	Eta11	GY	CV1	0.645	0.0121966	0.637	0.0196734	-0.008
Combinado Gaussiano	Eta8	Eta15	GY	CV2	0.989	0.0001216	0.991	0.0000983	0.002
Combinado arc-cosine	Eta9	Eta18	GY	CV2	0.966	0.0004052	0.970	0.0005673	0.004
Combinado linear	Eta7	Eta12	GY	CV2	0.980	0.0002151	0.982	0.0001190	0.002
Genômico Gaussiano	Eta3	Eta13	GY	CV2	0.973	0.0001704	0.974	0.0001302	0.001
Genômico arc-cosine	Eta5	Eta16	GY	CV2	0.964	0.0005435	0.968	0.0003975	0.004
Genômico linear	Eta1	Eta10	GY	CV2	0.982	0.0001184	0.984	0.0001054	0.002
Fenômico Gaussiano	Eta4	Eta14	GY	CV2	0.945	0.0015159	0.949	0.0012284	0.004
Fenômico arc-cosine	Eta6	Eta17	GY	CV2	0.695	0.0090873	0.692	0.0137389	-0.003
Fenômico linear	Eta2	Eta11	GY	CV2	0.967	0.0067725	0.933	0.0773312	-0.034
Combinado Gaussiano	Eta8	Eta15	KW	CV1	0.603	0.0099057	0.595	0.0156243	-0.008
Combinado arc-cosine	Eta9	Eta18	KW	CV1	0.555	0.0239686	0.548	0.0173955	-0.007
Combinado linear	Eta7	Eta12	KW	CV1	0.728	0.0145741	0.708	0.0150303	-0.019
Genômico Gaussiano	Eta3	Eta13	KW	CV1	0.602	0.0147757	0.595	0.0149952	-0.006
Genômico arc-cosine	Eta5	Eta16	KW	CV1	0.555	0.0199137	0.555	0.0168190	0.000
Genômico linear	Eta1	Eta10	KW	CV1	0.585	0.0151765	0.594	0.0179071	0.009
Fenômico Gaussiano	Eta4	Eta14	KW	CV1	0.218	0.0160546	0.203	0.0203866	-0.015
Fenômico arc-cosine	Eta6	Eta17	KW	CV1	0.271	0.0271355	0.243	0.0285264	-0.028
Fenômico linear	Eta2	Eta11	KW	CV1	0.762	0.0099625	0.761	0.0053242	0.000
Combinado Gaussiano	Eta8	Eta15	KW	CV2	0.993	0.0000316	0.994	0.0000388	0.001
Combinado arc-cosine	Eta9	Eta18	KW	CV2	0.977	0.0002182	0.978	0.0008926	0.001
Combinado linear	Eta7	Eta12	KW	CV2	0.994	0.0002515	0.994	0.0005010	0.000
Genômico Gaussiano	Eta3	Eta13	KW	CV2	0.982	0.0001168	0.983	0.0001170	0.001
Genômico arc-cosine	Eta5	Eta16	KW	CV2	0.974	0.0001071	0.975	0.0003408	0.001
Genômico linear	Eta1	Eta10	KW	CV2	0.989	0.0000573	0.990	0.0000506	0.001
Fenômico Gaussiano	Eta4	Eta14	KW	CV2	0.955	0.0008444	0.958	0.0010135	0.003
Fenômico arc-cosine	Eta6	Eta17	KW	CV2	0.772	0.0028779	0.771	0.0079429	-0.001
Fenômico linear	Eta2	Eta11	KW	CV2	0.989	0.0011764	0.986	0.0042936	-0.003

Este chunk resume como a inclusão do efeito climático altera o desempenho em CV0 e CV00. Essa comparação é especialmente informativa para entender a predição entre ambientes.

cvloo_w_effect %>%
  mutate( MeanWithoutW = round(MeanWithoutW, 3),
   MeanWithW = round(MeanWithW, 3),
    DeltaWithW = round(DeltaWithW, 3) ) %>%
  knitr::kable( caption = "Comparação pareada entre modelos sem e com o kernel climático (W) em CV0 e CV00.",
    align = "llllccccc" ) %>%
  kableExtra::kable_styling( bootstrap_options = c("striped", "hover", "condensed", "responsive"),
   full_width = FALSE,
    position = "left" ) %>%
  kableExtra::scroll_box(width = "100%", height = "320px")

Comparação pareada entre modelos sem e com o kernel climático (W) em CV0 e CV00.

Comparison	WithoutW	WithW	Trait	CV	Env_leave	MeanWithoutW	SDWithoutW	MeanWithW	SDWithW	DeltaWithW
Combinado Gaussiano	Eta8	Eta15	GY	CV0	CS11_WS	0.922	0.0006408	0.921	0.0005517	-0.001
Combinado arc-cosine	Eta9	Eta18	GY	CV0	CS11_WS	0.915	0.0064663	0.906	0.0159762	-0.008
Combinado linear	Eta7	Eta12	GY	CV0	CS11_WS	0.884	0.0160582	0.878	0.0198771	-0.006
Genômico Gaussiano	Eta3	Eta13	GY	CV0	CS11_WS	0.923	0.0004691	0.922	0.0003833	0.000
Genômico arc-cosine	Eta5	Eta16	GY	CV0	CS11_WS	0.926	0.0002212	0.924	0.0031137	-0.001
Genômico linear	Eta1	Eta10	GY	CV0	CS11_WS	0.921	0.0005552	0.916	0.0004999	-0.005
Fenômico Gaussiano	Eta4	Eta14	GY	CV0	CS11_WS	0.460	0.0046790	0.434	0.0085978	-0.026
Fenômico arc-cosine	Eta6	Eta17	GY	CV0	CS11_WS	0.421	0.0327846	0.398	0.0327840	-0.022
Fenômico linear	Eta2	Eta11	GY	CV0	CS11_WS	0.740	0.0417897	0.600	0.0937367	-0.140
Combinado Gaussiano	Eta8	Eta15	GY	CV0	CS11_WW	0.889	0.0007587	0.891	0.0012022	0.002
Combinado arc-cosine	Eta9	Eta18	GY	CV0	CS11_WW	0.848	0.0282177	0.850	0.0307368	0.003
Combinado linear	Eta7	Eta12	GY	CV0	CS11_WW	0.822	0.0652425	0.778	0.0747105	-0.043
Genômico Gaussiano	Eta3	Eta13	GY	CV0	CS11_WW	0.894	0.0005965	0.896	0.0004316	0.002
Genômico arc-cosine	Eta5	Eta16	GY	CV0	CS11_WW	0.885	0.0005851	0.886	0.0031297	0.000
Genômico linear	Eta1	Eta10	GY	CV0	CS11_WW	0.893	0.0005283	0.896	0.0004761	0.002
Fenômico Gaussiano	Eta4	Eta14	GY	CV0	CS11_WW	0.323	0.0079081	0.318	0.0074471	-0.005
Fenômico arc-cosine	Eta6	Eta17	GY	CV0	CS11_WW	0.310	0.0198619	0.275	0.0443295	-0.034
Fenômico linear	Eta2	Eta11	GY	CV0	CS11_WW	0.615	0.1492622	0.578	0.1292014	-0.038
Combinado Gaussiano	Eta8	Eta15	GY	CV0	CS12_WS	0.889	0.0008715	0.884	0.0007198	-0.005
Combinado arc-cosine	Eta9	Eta18	GY	CV0	CS12_WS	0.861	0.0164014	0.855	0.0214819	-0.006
Combinado linear	Eta7	Eta12	GY	CV0	CS12_WS	0.794	0.0574106	0.765	0.0375478	-0.029
Genômico Gaussiano	Eta3	Eta13	GY	CV0	CS12_WS	0.896	0.0007279	0.893	0.0011026	-0.003
Genômico arc-cosine	Eta5	Eta16	GY	CV0	CS12_WS	0.884	0.0006309	0.883	0.0006255	0.000
Genômico linear	Eta1	Eta10	GY	CV0	CS12_WS	0.894	0.0011375	0.885	0.0006739	-0.010
Fenômico Gaussiano	Eta4	Eta14	GY	CV0	CS12_WS	0.342	0.0078015	0.323	0.0078257	-0.020
Fenômico arc-cosine	Eta6	Eta17	GY	CV0	CS12_WS	0.273	0.0322555	0.257	0.0491397	-0.016
Fenômico linear	Eta2	Eta11	GY	CV0	CS12_WS	0.634	0.0757856	0.454	0.1025718	-0.180
Combinado Gaussiano	Eta8	Eta15	GY	CV0	CS12_WW	0.902	0.0005695	0.904	0.0004415	0.002
Combinado arc-cosine	Eta9	Eta18	GY	CV0	CS12_WW	0.892	0.0095946	0.894	0.0083430	0.003
Combinado linear	Eta7	Eta12	GY	CV0	CS12_WW	0.881	0.0134864	0.887	0.0098103	0.006
Genômico Gaussiano	Eta3	Eta13	GY	CV0	CS12_WW	0.904	0.0004100	0.907	0.0004159	0.004
Genômico arc-cosine	Eta5	Eta16	GY	CV0	CS12_WW	0.902	0.0004584	0.904	0.0005408	0.002
Genômico linear	Eta1	Eta10	GY	CV0	CS12_WW	0.904	0.0005270	0.909	0.0004207	0.005
Fenômico Gaussiano	Eta4	Eta14	GY	CV0	CS12_WW	0.359	0.0074689	0.350	0.0064624	-0.009
Fenômico arc-cosine	Eta6	Eta17	GY	CV0	CS12_WW	0.347	0.0265095	0.332	0.0285925	-0.015
Fenômico linear	Eta2	Eta11	GY	CV0	CS12_WW	0.592	0.1219799	0.512	0.1222827	-0.081
Combinado Gaussiano	Eta8	Eta15	GY	CV00	CS11_WS	0.719	0.0156232	0.716	0.0232356	-0.003
Combinado arc-cosine	Eta9	Eta18	GY	CV00	CS11_WS	0.702	0.0164750	0.715	0.0144928	0.014
Combinado linear	Eta7	Eta12	GY	CV00	CS11_WS	0.666	0.0336890	0.677	0.0357095	0.011
Genômico Gaussiano	Eta3	Eta13	GY	CV00	CS11_WS	0.717	0.0210367	0.716	0.0249825	-0.002
Genômico arc-cosine	Eta5	Eta16	GY	CV00	CS11_WS	0.732	0.0133004	0.735	0.0139247	0.003
Genômico linear	Eta1	Eta10	GY	CV00	CS11_WS	0.710	0.0164292	0.720	0.0158158	0.011
Fenômico Gaussiano	Eta4	Eta14	GY	CV00	CS11_WS	0.192	0.0265772	0.207	0.0534662	0.014
Fenômico arc-cosine	Eta6	Eta17	GY	CV00	CS11_WS	0.176	0.0470906	0.160	0.0463932	-0.017
Fenômico linear	Eta2	Eta11	GY	CV00	CS11_WS	0.547	0.0547123	0.468	0.0727954	-0.079
Combinado Gaussiano	Eta8	Eta15	GY	CV00	CS11_WW	0.734	0.0147333	0.734	0.0146172	0.000
Combinado arc-cosine	Eta9	Eta18	GY	CV00	CS11_WW	0.648	0.0420356	0.643	0.0355772	-0.005
Combinado linear	Eta7	Eta12	GY	CV00	CS11_WW	0.664	0.0551344	0.597	0.0889922	-0.067
Genômico Gaussiano	Eta3	Eta13	GY	CV00	CS11_WW	0.738	0.0106728	0.740	0.0125027	0.003
Genômico arc-cosine	Eta5	Eta16	GY	CV00	CS11_WW	0.724	0.0092225	0.725	0.0099241	0.000
Genômico linear	Eta1	Eta10	GY	CV00	CS11_WW	0.741	0.0102739	0.738	0.0121891	-0.002
Fenômico Gaussiano	Eta4	Eta14	GY	CV00	CS11_WW	0.167	0.0334714	0.192	0.0319594	0.025
Fenômico arc-cosine	Eta6	Eta17	GY	CV00	CS11_WW	0.128	0.0454014	0.126	0.0436496	-0.002
Fenômico linear	Eta2	Eta11	GY	CV00	CS11_WW	0.444	0.1253345	0.453	0.1196363	0.009
Combinado Gaussiano	Eta8	Eta15	GY	CV00	CS12_WS	0.722	0.0106525	0.727	0.0110813	0.005
Combinado arc-cosine	Eta9	Eta18	GY	CV00	CS12_WS	0.624	0.0366770	0.620	0.0290091	-0.004
Combinado linear	Eta7	Eta12	GY	CV00	CS12_WS	0.617	0.0577406	0.598	0.0459783	-0.019
Genômico Gaussiano	Eta3	Eta13	GY	CV00	CS12_WS	0.730	0.0105024	0.734	0.0068753	0.004
Genômico arc-cosine	Eta5	Eta16	GY	CV00	CS12_WS	0.693	0.0081952	0.690	0.0113171	-0.003
Genômico linear	Eta1	Eta10	GY	CV00	CS12_WS	0.725	0.0120592	0.733	0.0073064	0.008
Fenômico Gaussiano	Eta4	Eta14	GY	CV00	CS12_WS	0.132	0.0265251	0.129	0.0316274	-0.003
Fenômico arc-cosine	Eta6	Eta17	GY	CV00	CS12_WS	0.086	0.0531555	0.101	0.0517042	0.015
Fenômico linear	Eta2	Eta11	GY	CV00	CS12_WS	0.517	0.0691137	0.369	0.1174927	-0.148
Combinado Gaussiano	Eta8	Eta15	GY	CV00	CS12_WW	0.706	0.0153052	0.714	0.0127669	0.008
Combinado arc-cosine	Eta9	Eta18	GY	CV00	CS12_WW	0.656	0.0155661	0.656	0.0259759	0.000
Combinado linear	Eta7	Eta12	GY	CV00	CS12_WW	0.674	0.0268594	0.672	0.0388784	-0.001
Genômico Gaussiano	Eta3	Eta13	GY	CV00	CS12_WW	0.701	0.0098703	0.713	0.0086880	0.012
Genômico arc-cosine	Eta5	Eta16	GY	CV00	CS12_WW	0.691	0.0100686	0.682	0.0124059	-0.008
Genômico linear	Eta1	Eta10	GY	CV00	CS12_WW	0.696	0.0179032	0.710	0.0075409	0.013
Fenômico Gaussiano	Eta4	Eta14	GY	CV00	CS12_WW	0.119	0.0317911	0.114	0.0242938	-0.005
Fenômico arc-cosine	Eta6	Eta17	GY	CV00	CS12_WW	0.098	0.0427283	0.096	0.0740510	-0.002
Fenômico linear	Eta2	Eta11	GY	CV00	CS12_WW	0.410	0.1100689	0.386	0.0734259	-0.024
Combinado Gaussiano	Eta8	Eta15	KW	CV0	CS11_WS	0.959	0.0004251	0.955	0.0004242	-0.004
Combinado arc-cosine	Eta9	Eta18	KW	CV0	CS11_WS	0.952	0.0060164	0.934	0.0117906	-0.018
Combinado linear	Eta7	Eta12	KW	CV0	CS11_WS	0.928	0.0303599	0.904	0.0373431	-0.025
Genômico Gaussiano	Eta3	Eta13	KW	CV0	CS11_WS	0.960	0.0001845	0.953	0.0003107	-0.008
Genômico arc-cosine	Eta5	Eta16	KW	CV0	CS11_WS	0.961	0.0001712	0.955	0.0059577	-0.006
Genômico linear	Eta1	Eta10	KW	CV0	CS11_WS	0.960	0.0003318	0.945	0.0003220	-0.015
Fenômico Gaussiano	Eta4	Eta14	KW	CV0	CS11_WS	0.488	0.0064600	0.459	0.0053104	-0.029
Fenômico arc-cosine	Eta6	Eta17	KW	CV0	CS11_WS	0.514	0.0207448	0.529	0.0219561	0.015
Fenômico linear	Eta2	Eta11	KW	CV0	CS11_WS	0.800	0.0569121	0.708	0.0720481	-0.093
Combinado Gaussiano	Eta8	Eta15	KW	CV0	CS11_WW	0.886	0.0008546	0.888	0.0008502	0.002
Combinado arc-cosine	Eta9	Eta18	KW	CV0	CS11_WW	0.855	0.0194615	0.850	0.0175722	-0.005
Combinado linear	Eta7	Eta12	KW	CV0	CS11_WW	0.829	0.0365791	0.780	0.0736196	-0.048
Genômico Gaussiano	Eta3	Eta13	KW	CV0	CS11_WW	0.885	0.0011692	0.887	0.0010117	0.002
Genômico arc-cosine	Eta5	Eta16	KW	CV0	CS11_WW	0.874	0.0006841	0.873	0.0064145	-0.002
Genômico linear	Eta1	Eta10	KW	CV0	CS11_WW	0.885	0.0011861	0.889	0.0003511	0.004
Fenômico Gaussiano	Eta4	Eta14	KW	CV0	CS11_WW	0.331	0.0051097	0.327	0.0063766	-0.004
Fenômico arc-cosine	Eta6	Eta17	KW	CV0	CS11_WW	0.399	0.0311944	0.429	0.0314922	0.029
Fenômico linear	Eta2	Eta11	KW	CV0	CS11_WW	0.787	0.0671637	0.591	0.1578071	-0.196
Combinado Gaussiano	Eta8	Eta15	KW	CV0	CS12_WS	0.894	0.0011841	0.894	0.0009434	0.001
Combinado arc-cosine	Eta9	Eta18	KW	CV0	CS12_WS	0.860	0.0250670	0.880	0.0136477	0.020
Combinado linear	Eta7	Eta12	KW	CV0	CS12_WS	0.797	0.0640268	0.816	0.0478818	0.018
Genômico Gaussiano	Eta3	Eta13	KW	CV0	CS12_WS	0.898	0.0011057	0.899	0.0005593	0.001
Genômico arc-cosine	Eta5	Eta16	KW	CV0	CS12_WS	0.888	0.0005409	0.889	0.0010068	0.001
Genômico linear	Eta1	Eta10	KW	CV0	CS12_WS	0.896	0.0008220	0.897	0.0010396	0.001
Fenômico Gaussiano	Eta4	Eta14	KW	CV0	CS12_WS	0.410	0.0043472	0.400	0.0055684	-0.010
Fenômico arc-cosine	Eta6	Eta17	KW	CV0	CS12_WS	0.385	0.0316524	0.407	0.0309957	0.022
Fenômico linear	Eta2	Eta11	KW	CV0	CS12_WS	0.735	0.0917405	0.638	0.1303465	-0.097
Combinado Gaussiano	Eta8	Eta15	KW	CV0	CS12_WW	0.947	0.0004264	0.948	0.0003331	0.001
Combinado arc-cosine	Eta9	Eta18	KW	CV0	CS12_WW	0.938	0.0061385	0.935	0.0105933	-0.003
Combinado linear	Eta7	Eta12	KW	CV0	CS12_WW	0.922	0.0305936	0.928	0.0256338	0.006
Genômico Gaussiano	Eta3	Eta13	KW	CV0	CS12_WW	0.947	0.0002290	0.949	0.0002453	0.002
Genômico arc-cosine	Eta5	Eta16	KW	CV0	CS12_WW	0.947	0.0002183	0.946	0.0021284	-0.001
Genômico linear	Eta1	Eta10	KW	CV0	CS12_WW	0.947	0.0002957	0.948	0.0003126	0.002
Fenômico Gaussiano	Eta4	Eta14	KW	CV0	CS12_WW	0.431	0.0074450	0.429	0.0085145	-0.003
Fenômico arc-cosine	Eta6	Eta17	KW	CV0	CS12_WW	0.514	0.0186631	0.504	0.0290988	-0.010
Fenômico linear	Eta2	Eta11	KW	CV0	CS12_WW	0.824	0.0658355	0.746	0.0805916	-0.078
Combinado Gaussiano	Eta8	Eta15	KW	CV00	CS11_WS	0.586	0.0209643	0.566	0.0260974	-0.020
Combinado arc-cosine	Eta9	Eta18	KW	CV00	CS11_WS	0.508	0.0472672	0.484	0.0409598	-0.025
Combinado linear	Eta7	Eta12	KW	CV00	CS11_WS	0.654	0.0360532	0.608	0.0548281	-0.045
Genômico Gaussiano	Eta3	Eta13	KW	CV00	CS11_WS	0.578	0.0220801	0.573	0.0223042	-0.005
Genômico arc-cosine	Eta5	Eta16	KW	CV00	CS11_WS	0.544	0.0268564	0.548	0.0163422	0.004
Genômico linear	Eta1	Eta10	KW	CV00	CS11_WS	0.565	0.0200134	0.562	0.0174611	-0.003
Fenômico Gaussiano	Eta4	Eta14	KW	CV00	CS11_WS	0.218	0.0243076	0.202	0.0472486	-0.016
Fenômico arc-cosine	Eta6	Eta17	KW	CV00	CS11_WS	0.256	0.0252293	0.249	0.0365030	-0.007
Fenômico linear	Eta2	Eta11	KW	CV00	CS11_WS	0.662	0.0743260	0.624	0.0680554	-0.038
Combinado Gaussiano	Eta8	Eta15	KW	CV00	CS11_WW	0.566	0.0212727	0.562	0.0166412	-0.004
Combinado arc-cosine	Eta9	Eta18	KW	CV00	CS11_WW	0.460	0.0504655	0.451	0.0487862	-0.009
Combinado linear	Eta7	Eta12	KW	CV00	CS11_WW	0.544	0.0496768	0.488	0.0776466	-0.056
Genômico Gaussiano	Eta3	Eta13	KW	CV00	CS11_WW	0.565	0.0197142	0.567	0.0234297	0.002
Genômico arc-cosine	Eta5	Eta16	KW	CV00	CS11_WW	0.525	0.0245290	0.511	0.0208822	-0.014
Genômico linear	Eta1	Eta10	KW	CV00	CS11_WW	0.563	0.0179097	0.565	0.0130631	0.002
Fenômico Gaussiano	Eta4	Eta14	KW	CV00	CS11_WW	0.160	0.0141550	0.153	0.0236371	-0.007
Fenômico arc-cosine	Eta6	Eta17	KW	CV00	CS11_WW	0.222	0.0454472	0.172	0.0459169	-0.050
Fenômico linear	Eta2	Eta11	KW	CV00	CS11_WW	0.656	0.0537020	0.541	0.1089787	-0.115
Combinado Gaussiano	Eta8	Eta15	KW	CV00	CS12_WS	0.619	0.0125596	0.607	0.0129271	-0.012
Combinado arc-cosine	Eta9	Eta18	KW	CV00	CS12_WS	0.511	0.0392188	0.550	0.0346599	0.039
Combinado linear	Eta7	Eta12	KW	CV00	CS12_WS	0.553	0.0799328	0.552	0.0729139	-0.001
Genômico Gaussiano	Eta3	Eta13	KW	CV00	CS12_WS	0.616	0.0174929	0.611	0.0169350	-0.005
Genômico arc-cosine	Eta5	Eta16	KW	CV00	CS12_WS	0.560	0.0085485	0.569	0.0098365	0.009
Genômico linear	Eta1	Eta10	KW	CV00	CS12_WS	0.602	0.0156413	0.613	0.0088231	0.011
Fenômico Gaussiano	Eta4	Eta14	KW	CV00	CS12_WS	0.209	0.0273382	0.212	0.0227339	0.003
Fenômico arc-cosine	Eta6	Eta17	KW	CV00	CS12_WS	0.180	0.0753962	0.189	0.0409106	0.009
Fenômico linear	Eta2	Eta11	KW	CV00	CS12_WS	0.600	0.0894934	0.538	0.0973559	-0.062
Combinado Gaussiano	Eta8	Eta15	KW	CV00	CS12_WW	0.617	0.0102124	0.615	0.0248406	-0.002
Combinado arc-cosine	Eta9	Eta18	KW	CV00	CS12_WW	0.576	0.0306078	0.554	0.0430405	-0.022
Combinado linear	Eta7	Eta12	KW	CV00	CS12_WW	0.636	0.0411822	0.614	0.0398958	-0.022
Genômico Gaussiano	Eta3	Eta13	KW	CV00	CS12_WW	0.600	0.0272165	0.609	0.0237787	0.009
Genômico arc-cosine	Eta5	Eta16	KW	CV00	CS12_WW	0.591	0.0295687	0.585	0.0195874	-0.006
Genômico linear	Eta1	Eta10	KW	CV00	CS12_WW	0.601	0.0150357	0.603	0.0329311	0.001
Fenômico Gaussiano	Eta4	Eta14	KW	CV00	CS12_WW	0.197	0.0414129	0.198	0.0328087	0.002
Fenômico arc-cosine	Eta6	Eta17	KW	CV00	CS12_WW	0.239	0.0433968	0.256	0.0392577	0.017
Fenômico linear	Eta2	Eta11	KW	CV00	CS12_WW	0.640	0.0608590	0.602	0.0751970	-0.038

14. Tabelas de comparação entre famílias de kernel

Aqui o script contrasta os kernels linear, Gaussiano e arc-cosine dentro de cada via analítica.

Este chunk compara as principais famílias de kernel nos resumos de CV1 e CV2. Ele ajuda a distinguir se estruturas lineares, Gaussianas ou arc-cosine tendem a performar melhor nesses cenários.

cv12_kernel_family_compare %>%
  mutate( Linear = round(Linear, 3),
   Gaussian = round(Gaussian, 3),
   `Arc-cosine` = round(`Arc-cosine`, 3),
   Delta_Gaussian_vs_Linear = round(Delta_Gaussian_vs_Linear, 3),
   Delta_ArcCos_vs_Linear = round(Delta_ArcCos_vs_Linear, 3),
    Delta_ArcCos_vs_Gaussian = round(Delta_ArcCos_vs_Gaussian, 3) ) %>%
  knitr::kable( caption = "Comparação entre famílias de kernel dentro de cada via analítica em CV1 e CV2.",
    align = "llllcccccc" ) %>%
  kableExtra::kable_styling( bootstrap_options = c("striped", "hover", "condensed", "responsive"),
   full_width = FALSE,
    position = "left" ) %>%
  kableExtra::scroll_box(width = "100%", height = "320px")

Comparação entre famílias de kernel dentro de cada via analítica em CV1 e CV2.

Trait	CV	Pathway	WeatherStatus	Linear	Gaussian	Arc-cosine	Delta_Gaussian_vs_Linear	Delta_ArcCos_vs_Linear	Delta_ArcCos_vs_Gaussian
GY	CV1	Via combinada G + P	Com W	0.719	0.723	0.706	0.004	-0.013	-0.017
GY	CV1	Via combinada G + P	Sem W	0.713	0.717	0.703	0.004	-0.010	-0.014
GY	CV1	Via genômica	Com W	0.725	0.727	0.714	0.003	-0.011	-0.014
GY	CV1	Via genômica	Sem W	0.719	0.719	0.711	0.000	-0.008	-0.008
GY	CV1	Via fenômica	Com W	0.637	0.147	0.177	-0.490	-0.460	0.030
GY	CV1	Via fenômica	Sem W	0.645	0.134	0.178	-0.510	-0.467	0.043
GY	CV2	Via combinada G + P	Com W	0.982	0.991	0.970	0.008	-0.012	-0.021
GY	CV2	Via combinada G + P	Sem W	0.980	0.989	0.966	0.008	-0.014	-0.023
GY	CV2	Via genômica	Com W	0.984	0.974	0.968	-0.010	-0.016	-0.006
GY	CV2	Via genômica	Sem W	0.982	0.973	0.964	-0.009	-0.019	-0.009
GY	CV2	Via fenômica	Com W	0.933	0.949	0.692	0.016	-0.241	-0.257
GY	CV2	Via fenômica	Sem W	0.967	0.945	0.695	-0.022	-0.272	-0.249
KW	CV1	Via combinada G + P	Com W	0.708	0.595	0.548	-0.113	-0.160	-0.047
KW	CV1	Via combinada G + P	Sem W	0.728	0.603	0.555	-0.124	-0.173	-0.048
KW	CV1	Via genômica	Com W	0.594	0.595	0.555	0.002	-0.038	-0.040
KW	CV1	Via genômica	Sem W	0.585	0.602	0.555	0.017	-0.030	-0.047
KW	CV1	Via fenômica	Com W	0.761	0.203	0.243	-0.558	-0.519	0.040
KW	CV1	Via fenômica	Sem W	0.762	0.218	0.271	-0.544	-0.491	0.054
KW	CV2	Via combinada G + P	Com W	0.994	0.994	0.978	0.000	-0.016	-0.016
KW	CV2	Via combinada G + P	Sem W	0.994	0.993	0.977	-0.001	-0.017	-0.016
KW	CV2	Via genômica	Com W	0.990	0.983	0.975	-0.007	-0.015	-0.008
KW	CV2	Via genômica	Sem W	0.989	0.982	0.974	-0.007	-0.015	-0.008
KW	CV2	Via fenômica	Com W	0.986	0.958	0.771	-0.028	-0.215	-0.187
KW	CV2	Via fenômica	Sem W	0.989	0.955	0.772	-0.034	-0.217	-0.183

Este chunk compara as principais famílias de kernel nos resumos de CV0 e CV00. O objetivo é entender se a estrutura de kernel preferida muda quando o problema de predição se torna ambientalmente mais rigoroso.

cvloo_kernel_family_compare %>%
  mutate( Linear = round(Linear, 3),
   Gaussian = round(Gaussian, 3),
   `Arc-cosine` = round(`Arc-cosine`, 3),
   Delta_Gaussian_vs_Linear = round(Delta_Gaussian_vs_Linear, 3),
   Delta_ArcCos_vs_Linear = round(Delta_ArcCos_vs_Linear, 3),
    Delta_ArcCos_vs_Gaussian = round(Delta_ArcCos_vs_Gaussian, 3) ) %>%
  knitr::kable( caption = "Comparação entre famílias de kernel dentro de cada via analítica em CV0 e CV00.",
    align = "lllllcccccc" ) %>%
  kableExtra::kable_styling( bootstrap_options = c("striped", "hover", "condensed", "responsive"),
   full_width = FALSE,
    position = "left" ) %>%
  kableExtra::scroll_box(width = "100%", height = "320px")

Comparação entre famílias de kernel dentro de cada via analítica em CV0 e CV00.

Trait	CV	Env_leave	Pathway	WeatherStatus	Linear	Gaussian	Arc-cosine	Delta_Gaussian_vs_Linear	Delta_ArcCos_vs_Linear	Delta_ArcCos_vs_Gaussian
GY	CV0	CS11_WS	Via combinada G + P	Com W	0.878	0.921	0.906	0.042	0.028	-0.014
GY	CV0	CS11_WS	Via combinada G + P	Sem W	0.884	0.922	0.915	0.037	0.030	-0.007
GY	CV0	CS11_WS	Via genômica	Com W	0.916	0.922	0.924	0.006	0.008	0.002
GY	CV0	CS11_WS	Via genômica	Sem W	0.921	0.923	0.926	0.001	0.004	0.003
GY	CV0	CS11_WS	Via fenômica	Com W	0.600	0.434	0.398	-0.166	-0.201	-0.036
GY	CV0	CS11_WS	Via fenômica	Sem W	0.740	0.460	0.421	-0.280	-0.319	-0.039
GY	CV0	CS11_WW	Via combinada G + P	Com W	0.778	0.891	0.850	0.113	0.072	-0.041
GY	CV0	CS11_WW	Via combinada G + P	Sem W	0.822	0.889	0.848	0.067	0.026	-0.041
GY	CV0	CS11_WW	Via genômica	Com W	0.896	0.896	0.886	0.000	-0.010	-0.010
GY	CV0	CS11_WW	Via genômica	Sem W	0.893	0.894	0.885	0.001	-0.008	-0.009
GY	CV0	CS11_WW	Via fenômica	Com W	0.578	0.318	0.275	-0.259	-0.302	-0.043
GY	CV0	CS11_WW	Via fenômica	Sem W	0.615	0.323	0.310	-0.292	-0.305	-0.013
GY	CV0	CS12_WS	Via combinada G + P	Com W	0.765	0.884	0.855	0.119	0.090	-0.029
GY	CV0	CS12_WS	Via combinada G + P	Sem W	0.794	0.889	0.861	0.095	0.067	-0.028
GY	CV0	CS12_WS	Via genômica	Com W	0.885	0.893	0.883	0.008	-0.001	-0.009
GY	CV0	CS12_WS	Via genômica	Sem W	0.894	0.896	0.884	0.002	-0.011	-0.012
GY	CV0	CS12_WS	Via fenômica	Com W	0.454	0.323	0.257	-0.131	-0.196	-0.065
GY	CV0	CS12_WS	Via fenômica	Sem W	0.634	0.342	0.273	-0.292	-0.361	-0.069
GY	CV0	CS12_WW	Via combinada G + P	Com W	0.887	0.904	0.894	0.017	0.007	-0.009
GY	CV0	CS12_WW	Via combinada G + P	Sem W	0.881	0.902	0.892	0.021	0.011	-0.010
GY	CV0	CS12_WW	Via genômica	Com W	0.909	0.907	0.904	-0.002	-0.005	-0.003
GY	CV0	CS12_WW	Via genômica	Sem W	0.904	0.904	0.902	-0.001	-0.002	-0.001
GY	CV0	CS12_WW	Via fenômica	Com W	0.512	0.350	0.332	-0.161	-0.180	-0.018
GY	CV0	CS12_WW	Via fenômica	Sem W	0.592	0.359	0.347	-0.234	-0.246	-0.012
GY	CV00	CS11_WS	Via combinada G + P	Com W	0.677	0.716	0.715	0.039	0.038	-0.001
GY	CV00	CS11_WS	Via combinada G + P	Sem W	0.666	0.719	0.702	0.053	0.036	-0.017
GY	CV00	CS11_WS	Via genômica	Com W	0.720	0.716	0.735	-0.005	0.015	0.019
GY	CV00	CS11_WS	Via genômica	Sem W	0.710	0.717	0.732	0.007	0.023	0.015
GY	CV00	CS11_WS	Via fenômica	Com W	0.468	0.207	0.160	-0.261	-0.308	-0.047
GY	CV00	CS11_WS	Via fenômica	Sem W	0.547	0.192	0.176	-0.355	-0.371	-0.016
GY	CV00	CS11_WW	Via combinada G + P	Com W	0.597	0.734	0.643	0.137	0.046	-0.091
GY	CV00	CS11_WW	Via combinada G + P	Sem W	0.664	0.734	0.648	0.070	-0.016	-0.086
GY	CV00	CS11_WW	Via genômica	Com W	0.738	0.740	0.725	0.002	-0.014	-0.016
GY	CV00	CS11_WW	Via genômica	Sem W	0.741	0.738	0.724	-0.003	-0.016	-0.013
GY	CV00	CS11_WW	Via fenômica	Com W	0.453	0.192	0.126	-0.261	-0.327	-0.066
GY	CV00	CS11_WW	Via fenômica	Sem W	0.444	0.167	0.128	-0.277	-0.316	-0.039
GY	CV00	CS12_WS	Via combinada G + P	Com W	0.598	0.727	0.620	0.129	0.022	-0.107
GY	CV00	CS12_WS	Via combinada G + P	Sem W	0.617	0.722	0.624	0.105	0.007	-0.098
GY	CV00	CS12_WS	Via genômica	Com W	0.733	0.734	0.690	0.001	-0.044	-0.044
GY	CV00	CS12_WS	Via genômica	Sem W	0.725	0.730	0.693	0.005	-0.033	-0.037
GY	CV00	CS12_WS	Via fenômica	Com W	0.369	0.129	0.101	-0.240	-0.268	-0.028
GY	CV00	CS12_WS	Via fenômica	Sem W	0.517	0.132	0.086	-0.385	-0.431	-0.046
GY	CV00	CS12_WW	Via combinada G + P	Com W	0.672	0.714	0.656	0.042	-0.016	-0.058
GY	CV00	CS12_WW	Via combinada G + P	Sem W	0.674	0.706	0.656	0.032	-0.018	-0.050
GY	CV00	CS12_WW	Via genômica	Com W	0.710	0.713	0.682	0.003	-0.027	-0.031
GY	CV00	CS12_WW	Via genômica	Sem W	0.696	0.701	0.691	0.005	-0.006	-0.011
GY	CV00	CS12_WW	Via fenômica	Com W	0.386	0.114	0.096	-0.272	-0.290	-0.018
GY	CV00	CS12_WW	Via fenômica	Sem W	0.410	0.119	0.098	-0.291	-0.312	-0.021
KW	CV0	CS11_WS	Via combinada G + P	Com W	0.904	0.955	0.934	0.051	0.030	-0.021
KW	CV0	CS11_WS	Via combinada G + P	Sem W	0.928	0.959	0.952	0.031	0.024	-0.007
KW	CV0	CS11_WS	Via genômica	Com W	0.945	0.953	0.955	0.008	0.010	0.002
KW	CV0	CS11_WS	Via genômica	Sem W	0.960	0.960	0.961	0.000	0.001	0.001
KW	CV0	CS11_WS	Via fenômica	Com W	0.708	0.459	0.529	-0.249	-0.179	0.070
KW	CV0	CS11_WS	Via fenômica	Sem W	0.800	0.488	0.514	-0.312	-0.287	0.025
KW	CV0	CS11_WW	Via combinada G + P	Com W	0.780	0.888	0.850	0.108	0.070	-0.038
KW	CV0	CS11_WW	Via combinada G + P	Sem W	0.829	0.886	0.855	0.057	0.026	-0.031
KW	CV0	CS11_WW	Via genômica	Com W	0.889	0.887	0.873	-0.002	-0.016	-0.015
KW	CV0	CS11_WW	Via genômica	Sem W	0.885	0.885	0.874	0.000	-0.011	-0.011
KW	CV0	CS11_WW	Via fenômica	Com W	0.591	0.327	0.429	-0.263	-0.162	0.101
KW	CV0	CS11_WW	Via fenômica	Sem W	0.787	0.331	0.399	-0.455	-0.387	0.068
KW	CV0	CS12_WS	Via combinada G + P	Com W	0.816	0.894	0.880	0.079	0.064	-0.015
KW	CV0	CS12_WS	Via combinada G + P	Sem W	0.797	0.894	0.860	0.097	0.062	-0.034
KW	CV0	CS12_WS	Via genômica	Com W	0.897	0.899	0.889	0.002	-0.009	-0.011
KW	CV0	CS12_WS	Via genômica	Sem W	0.896	0.898	0.888	0.002	-0.008	-0.010
KW	CV0	CS12_WS	Via fenômica	Com W	0.638	0.400	0.407	-0.238	-0.231	0.007
KW	CV0	CS12_WS	Via fenômica	Sem W	0.735	0.410	0.385	-0.325	-0.350	-0.025
KW	CV0	CS12_WW	Via combinada G + P	Com W	0.928	0.948	0.935	0.020	0.007	-0.013
KW	CV0	CS12_WW	Via combinada G + P	Sem W	0.922	0.947	0.938	0.025	0.015	-0.010
KW	CV0	CS12_WW	Via genômica	Com W	0.948	0.949	0.946	0.001	-0.002	-0.003
KW	CV0	CS12_WW	Via genômica	Sem W	0.947	0.947	0.947	0.001	0.000	0.000
KW	CV0	CS12_WW	Via fenômica	Com W	0.746	0.429	0.504	-0.318	-0.242	0.076
KW	CV0	CS12_WW	Via fenômica	Sem W	0.824	0.431	0.514	-0.393	-0.310	0.083
KW	CV00	CS11_WS	Via combinada G + P	Com W	0.608	0.566	0.484	-0.042	-0.125	-0.083
KW	CV00	CS11_WS	Via combinada G + P	Sem W	0.654	0.586	0.508	-0.068	-0.145	-0.078
KW	CV00	CS11_WS	Via genômica	Com W	0.562	0.573	0.548	0.010	-0.015	-0.025
KW	CV00	CS11_WS	Via genômica	Sem W	0.565	0.578	0.544	0.012	-0.021	-0.034
KW	CV00	CS11_WS	Via fenômica	Com W	0.624	0.202	0.249	-0.422	-0.375	0.047
KW	CV00	CS11_WS	Via fenômica	Sem W	0.662	0.218	0.256	-0.444	-0.407	0.038
KW	CV00	CS11_WW	Via combinada G + P	Com W	0.488	0.562	0.451	0.073	-0.037	-0.111
KW	CV00	CS11_WW	Via combinada G + P	Sem W	0.544	0.566	0.460	0.021	-0.085	-0.106
KW	CV00	CS11_WW	Via genômica	Com W	0.565	0.567	0.511	0.002	-0.054	-0.056
KW	CV00	CS11_WW	Via genômica	Sem W	0.563	0.565	0.525	0.001	-0.039	-0.040
KW	CV00	CS11_WW	Via fenômica	Com W	0.541	0.153	0.172	-0.388	-0.369	0.019
KW	CV00	CS11_WW	Via fenômica	Sem W	0.656	0.160	0.222	-0.496	-0.434	0.062
KW	CV00	CS12_WS	Via combinada G + P	Com W	0.552	0.607	0.550	0.055	-0.002	-0.057
KW	CV00	CS12_WS	Via combinada G + P	Sem W	0.553	0.619	0.511	0.066	-0.042	-0.108
KW	CV00	CS12_WS	Via genômica	Com W	0.613	0.611	0.569	-0.001	-0.044	-0.043
KW	CV00	CS12_WS	Via genômica	Sem W	0.602	0.616	0.560	0.014	-0.042	-0.056
KW	CV00	CS12_WS	Via fenômica	Com W	0.538	0.212	0.189	-0.327	-0.349	-0.022
KW	CV00	CS12_WS	Via fenômica	Sem W	0.600	0.209	0.180	-0.391	-0.420	-0.029
KW	CV00	CS12_WW	Via combinada G + P	Com W	0.614	0.615	0.554	0.001	-0.061	-0.061
KW	CV00	CS12_WW	Via combinada G + P	Sem W	0.636	0.617	0.576	-0.019	-0.060	-0.041
KW	CV00	CS12_WW	Via genômica	Com W	0.603	0.609	0.585	0.006	-0.018	-0.024
KW	CV00	CS12_WW	Via genômica	Sem W	0.601	0.600	0.591	-0.001	-0.010	-0.009
KW	CV00	CS12_WW	Via fenômica	Com W	0.602	0.198	0.256	-0.404	-0.346	0.057
KW	CV00	CS12_WW	Via fenômica	Sem W	0.640	0.197	0.239	-0.443	-0.401	0.042

15. Figuras

As figuras finais resumem a capacidade preditiva para o catálogo completo de modelos em CV1/CV2 e CV0/CV00.

Este chunk cria a figura que resume o desempenho dos modelos em CV1 e CV2. O gráfico oferece um ranking visual compacto para os cenários de partição genotípica.

cv12_plot_data <- cv12_summary %>%
  mutate( ShortDescription = factor(ShortDescription, levels = model_catalog$ShortDescription) )
lower_limit_cv12 <- min(cv12_plot_data$MeanCorrelation - cv12_plot_data$SDCorrelation, na.rm = TRUE)
upper_limit_cv12 <- max(cv12_plot_data$MeanCorrelation + cv12_plot_data$SDCorrelation, na.rm = TRUE)
p_cv12 <- ggplot(cv12_plot_data, aes(x = ShortDescription, y = MeanCorrelation, fill = Group)) +
  geom_col(width = 0.75, color = "grey30") +
  geom_errorbar( aes( ymin = MeanCorrelation - SDCorrelation, ymax = MeanCorrelation + SDCorrelation ),
   width = 0.20,
    linewidth = 0.35 ) +
  facet_grid(Trait ~ CV) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey40") +
  coord_cartesian(ylim = c(lower_limit_cv12 - 0.03, upper_limit_cv12 + 0.03)) +
  scale_x_discrete(name = "Descrição do modelo") +
  scale_y_continuous(name = "Correlação preditiva") +
  ggthemes::scale_fill_gdocs(name = "Grupo de comparação") +
  theme_bw() +
  theme( plot.title = element_text(face = "bold", size = 12, hjust = 0.5),
   strip.text = element_text(face = "bold", size = 10),
   axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7),
   axis.text.y = element_text(size = 9),
   axis.title = element_text(size = 10),
   legend.position = "top",
   legend.title = element_text(size = 9),
    legend.text = element_text(size = 8) ) +
  labs(title = "Capacidade preditiva em CV1 e CV2")
p_cv12

Past versions of figure-cv12-1.png

Version	Author	Date
6ce48a3	WevertonGomesCosta	2026-04-08
6b32571	WevertonGomesCosta	2026-04-05

ggsave(file.path(figures_dir, "prediction_cv1_cv2.tiff"),
 plot = p_cv12,
 width = 14,
 height = 8,
 dpi = 300,
  bg = "white")

Este chunk cria a figura que resume o desempenho dos modelos em CV0 e CV00. O gráfico destaca como o ranking dos modelos muda sob validação leave-one-environment-out.

cvloo_plot_data <- cvloo_summary %>%
  mutate( ShortDescription = factor(ShortDescription, levels = model_catalog$ShortDescription),
    Env_leave = factor(Env_leave, levels = c("CS11_WS", "CS11_WW", "CS12_WS", "CS12_WW")) )
lower_limit_cvloo <- min(cvloo_plot_data$MeanCorrelation - cvloo_plot_data$SDCorrelation, na.rm = TRUE)
upper_limit_cvloo <- max(cvloo_plot_data$MeanCorrelation + cvloo_plot_data$SDCorrelation, na.rm = TRUE)
p_cvloo <- ggplot(cvloo_plot_data, aes(x = ShortDescription, y = MeanCorrelation, fill = Group)) +
  geom_col(width = 0.75, color = "grey30") +
  geom_errorbar( aes( ymin = MeanCorrelation - SDCorrelation, ymax = MeanCorrelation + SDCorrelation ),
   width = 0.20,
   linewidth = 0.35,
    na.rm = TRUE ) +
  facet_grid(Env_leave ~ Trait + CV) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey40") +
  coord_cartesian(ylim = c(lower_limit_cvloo - 0.03, upper_limit_cvloo + 0.03)) +
  scale_x_discrete(name = "Descrição do modelo") +
  scale_y_continuous(name = "Correlação preditiva") +
  ggthemes::scale_fill_gdocs(name = "Grupo de comparação") +
  theme_bw() +
  theme( plot.title = element_text(face = "bold", size = 12, hjust = 0.5),
   strip.text = element_text(face = "bold", size = 9),
   axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6),
   axis.text.y = element_text(size = 8),
   axis.title = element_text(size = 10),
   legend.position = "top",
   legend.title = element_text(size = 9),
    legend.text = element_text(size = 8) ) +
  labs(title = "Capacidade preditiva em CV0 e CV00")
p_cvloo

Past versions of figure-cvloo-1.png

Version	Author	Date
6ce48a3	WevertonGomesCosta	2026-04-08
6b32571	WevertonGomesCosta	2026-04-05

ggsave(file.path(figures_dir, "prediction_cv0_cv00.tiff"),
 plot = p_cvloo,
 width = 16,
 height = 12,
 dpi = 300,
  bg = "white")

16. Nota final

Esta versão didática está alinhada ao fluxo atual de predição baseado em analysis_prediction_run_outputs.Rmd. Ela espera arquivos nos quais:

• a repetição e o fold estão codificados tanto na hierarquia de pastas quanto no nome do arquivo;
• CV0 e CV00 carregam o rótulo do ambiente deixado de fora tanto na hierarquia de pastas quanto no nome do arquivo;
• os CSVs de predição são lidos recursivamente a partir de output/results/;
• CV0 e CV00 ainda exigem o filtro Env == Env_leave para os resumos do ambiente deixado de fora.

Depois de executar este arquivo, a próxima etapa no repositório é rodar o script orientado ao manuscrito que consome as tabelas-resumo gravadas em output/tables/.

Navegação entre etapas

Etapa anterior: Predição — Parte II
Próxima referência no site: Home

Fora do menu do site, a próxima etapa analítica é gerar os resumos orientados ao manuscrito e as tabelas de revisão a partir das saídas criadas aqui.

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] stringr_1.6.0    kableExtra_1.4.0 knitr_1.50       ggthemes_5.2.0  
[5] ggplot2_4.0.2    tibble_3.3.1     readr_2.2.0      tidyr_1.3.2     
[9] dplyr_1.2.0     

loaded via a namespace (and not attached):
 [1] sass_0.4.10        generics_0.1.4     xml2_1.5.1         stringi_1.8.7     
 [5] hms_1.1.4          digest_0.6.39      magrittr_2.0.4     evaluate_1.0.5    
 [9] grid_4.5.1         RColorBrewer_1.1-3 fastmap_1.2.0      rprojroot_2.1.1   
[13] workflowr_1.7.2    jsonlite_2.0.0     whisker_0.4.1      promises_1.5.0    
[17] purrr_1.2.1        viridisLite_0.4.2  scales_1.4.0       textshaping_1.0.4 
[21] jquerylib_0.1.4    cli_3.6.5          crayon_1.5.3       rlang_1.1.7       
[25] bit64_4.6.0-1      withr_3.0.2        cachem_1.1.0       yaml_2.3.12       
[29] otel_0.2.0         parallel_4.5.1     tools_4.5.1        tzdb_0.5.0        
[33] httpuv_1.6.16      vctrs_0.7.1        R6_2.6.1           lifecycle_1.0.5   
[37] git2r_0.36.2       bit_4.6.0          fs_1.6.6           vroom_1.7.0       
[41] ragg_1.5.0         pkgconfig_2.0.3    pillar_1.11.1      bslib_0.9.0       
[45] later_1.4.4        gtable_0.3.6       glue_1.8.0         Rcpp_1.1.1        
[49] systemfonts_1.3.1  xfun_0.54          tidyselect_1.2.1   rstudioapi_0.17.1 
[53] farver_2.1.2       htmltools_0.5.9    labeling_0.4.3     rmarkdown_2.30    
[57] svglite_2.2.2      compiler_4.5.1     S7_0.2.1