Prediction analyses - Part I: setup, models, and CV design

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Last updated: 2026-04-08

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Knit directory: Integrating-nir-genomic-kernel/

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R Markdown file: up-to-date

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Seed: set.seed(20250829)

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Session information: recorded

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Repository version: 88e9ac3

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Language / Idioma: English | Português

Module navigation / Navegação do módulo: Part II: run prediction and outputs

1. Introduction

Prediction is the stage of the pipeline in which the objects produced earlier are reorganized into a model-fitting framework. At this point, the goal is not yet to run the full prediction workflow. Instead, this module prepares the analytical structure that will later be used in the execution step.

The prediction problem studied here is not limited to fitting a single model. The project compares a reduced catalog of 18 models built from different combinations of environmental information (E), genomic information (G), phenomic information (P), weather-derived information (W), and their corresponding linear or kernel-based interaction structures. Because these models are intended to answer different prediction questions under different validation settings, the first step is to make their organization explicit and understandable before any expensive computation is performed.

This file therefore serves two complementary purposes. First, it prepares the inputs and computational settings needed for prediction. Second, it explains the analytical meaning of the model catalog and of the cross-validation schemes adopted in the study. In practical terms, this means that the reader should leave this module understanding what is being predicted, which sources of information are being compared, and how prediction quality will later be assessed.

Inputs, outputs, and the role of this stage

This stage receives as input the objects created in matrizes.Rmd. These include the phenotypic data table (Pheno), the incidence structures that connect observations to environments and weather, and the linear and nonlinear kernels derived from genomic and phenomic information. Together, these objects provide the full representation needed to define the prediction models.

The main output of this file is not a final result table. Instead, the output is an organized prediction framework: the computational settings, the catalog of 18 models, the corresponding Eta_list specifications, the validation design, and the naming convention that will later be used to store prediction results. In other words, this module prepares the prediction system that will be executed in the next stage.

This distinction is important for didactic reasons. Here we focus on understanding the structure of the prediction analysis: what enters each model, how the models differ, and what each validation scenario is designed to test. The execution itself is documented in the next module, where the prediction outputs are generated, inventoried, and prepared for downstream summaries.

Inputs - Phenotypic observations and identifiers stored in Pheno - Environmental and weather design structures - Linear incidence-based relationships - Nonlinear genomic and phenomic kernels - Derived interaction kernels generated in matrizes.Rmd

Outputs - General computational settings for model fitting - The 18-model catalog used in the study - The full Eta_list structure that defines each prediction model - The cross-validation design used to evaluate prediction performance - The file-naming rules used downstream in execution, inventory, and visualization

Previous stage: variance_components.Rmd
Next stage: analysis_prediction_run_outputs.Rmd

2. Packages and options

Before defining any prediction model, it is useful to begin from a clean and reproducible working session. In a teaching-oriented script, this first step also has a practical function: it makes clear that model specification should not depend on objects left from previous runs.

At this stage, the emphasis is still on organization rather than execution. The prediction models are introduced, compared, and translated into their ETA structures, but the expensive fitting routine is left for the execution module. For that reason, the setup here is intentionally minimal. Scientific notation is turned off to improve readability of printed values, the workspace is cleared so that the objects used later in the file are those explicitly loaded from the pipeline outputs, and the parallel package is declared because the number of processor cores is already defined at this stage.

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

library(parallel)

3. Inputs generated by previous scripts

The prediction analysis starts from the matrices and kernels already generated in matrizes.Rmd. This means that the current stage does not rebuild the data representation from scratch. Instead, it imports the saved objects that now serve as the analytical foundation for model specification.

These objects represent different parts of the problem. Pheno contains the observation-level structure of the study, including traits, genotypes, and environments. ZG and ZP represent the linear genomic and phenomic relationships. ZE represents the categorical environment structure, whereas ZW carries the weather-derived information. The remaining objects correspond to interactions and nonlinear kernels that allow the comparison of simpler and more flexible prediction models.

Reading these objects directly from output/Matrizes/ is important for two reasons. First, it keeps the workflow reproducible by making prediction depend on artifacts already created in the previous stage. Second, it allows the reader to see how the prediction module is connected to the harmonization and kernel-construction module.

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")

In the current version of the pipeline, the observation-level matrices produced in matrizes.Rmd use the names ZE, ZGZE, ZPZE, ZGZW, and ZPZW to represent the categorical environment design and the interaction structures that link genomic and phenomic information to environment and weather. Keeping these names explicit here helps the reader connect the saved files to the prediction structures that will be built later in this module.

4. MCMC and general settings

Once the inputs are available, the next step is to define the computational settings that control model fitting and repeated validation. These values do not change the scientific question being asked, but they determine how the posterior distributions are explored and how stable the repeated prediction summaries can be.

The MCMC settings (nIter, burnIn, and thin) define how the Bayesian estimation is run. The number of repetitions (num_rep) controls how many independent partitions will later be used in the validation schemes. The number of processor cores (n_cores) is recorded here because the execution module can use parallel computation when the expensive prediction stage is activated.

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. Model catalog

The prediction stage does not compare a single specification. Instead, it evaluates a reduced catalog of 18 models that represent different ways of combining environment (E), genomic information (G), phenomic information (P), weather-derived information (W), and their corresponding interaction structures. This catalog is useful because it allows the reader to see, in a single place, how the analytical comparison is organized before the models are fitted.

The table below should be read as a map of the modeling strategy adopted in the study. Each row identifies one model, gives it a stable label, and summarizes its main composition. These labels will be used again in the execution step, in the output files, and later in the visualization module. For that reason, understanding the catalog here makes the rest of the prediction workflow much easier to follow.

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(
    "Linear genomic model with environment and G × E interaction",
    "Linear phenomic model with environment and P × E interaction",
    "Gaussian genomic model with environment and GGK × E interaction",
    "Gaussian phenomic model with environment and PGK × E interaction",
    "Arc-cosine genomic model with environment and GAK × E interaction",
    "Arc-cosine phenomic model with environment and PAK × E interaction",
    "Linear combined genomic + phenomic model with environment, GE, and PE",
    "Gaussian combined genomic + phenomic model with environment, GGKE, and PGKE",
    "Arc-cosine combined genomic + phenomic model with environment, GAKE, and PAKE",
    "Linear genomic-weather model with environment, weather, GE, and GW",
    "Linear phenomic-weather model with environment, weather, PE, and PW",
    "Linear combined genomic + phenomic + weather model with GE, PE, GW, and PW",
    "Gaussian genomic-weather model with environment, weather, GGKE, and GGKW",
    "Gaussian phenomic-weather model with environment, weather, PGKE, and PGKW",
    "Gaussian combined genomic + phenomic + weather model with GGKE, PGKE, GGKW, and PGKW",
    "Arc-cosine genomic-weather model with environment, weather, GAKE, and GAKW",
    "Arc-cosine phenomic-weather model with environment, weather, PAKE, and PAKW",
    "Arc-cosine combined genomic + phenomic + weather model with GAKE, PAKE, GAKW, and PAKW"
  ),
  stringsAsFactors = FALSE
)

model_catalog

6. Model specification

Once the catalog is defined, the next step is to translate each model into the structure that will be passed to BGLR. In this workflow, that structure is stored in Eta_list. Each entry of Eta_list corresponds to one model from the catalog and records which effects or kernels will be used when the model is later fitted.

This part of the tutorial is important because it connects the biological meaning of the models to their computational implementation. The code below does not introduce a new scientific question; instead, it makes explicit how each analytical idea is encoded in a form that the prediction step can use. To keep this organization readable, the 18 models are presented in small conceptual groups.

6.1 Initialize the ETA container

The first step is simply to create the object that will hold all model specifications. Starting with an empty container makes it easier to add the models one group at a time and to keep the final structure aligned with the catalog presented above.

Eta_list <- list()

6.2 Models 1 to 6: single-source pathways with E

The first six models establish the basic comparison framework of the study. In all of them, the categorical effect of environment is retained, and this environmental baseline is then combined with one main source of biological information and its corresponding interaction with environment. These models are useful because they show how prediction behaves when genomic, phenomic, or kernel-based information is considered separately rather than in integrated form.

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 Models 7 to 9: integrated genomic and phenomic pathways without W

The next three models move from isolated information sources to combined pathways. Here the goal is to evaluate whether integrating genomic and phenomic information improves predictive performance before weather-derived information is introduced. Conceptually, this group helps the reader compare single-source models against multi-source models while keeping the weather component out of the equation.

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 Models 10 to 12: weather-augmented linear pathways

Models 10 to 12 introduce weather-derived information into the prediction structure. At this point, the comparison is no longer restricted to genomic and phenomic sources alone. The purpose of this group is to show how prediction changes when climatic similarity is incorporated together with linear genomic or phenomic relationships and their interactions.

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 Models 13 to 18: weather-augmented nonlinear pathways

The final six models extend the previous comparison by combining weather-derived information with nonlinear kernel structures. These models are important because they represent the most flexible specifications in the catalog. In practical terms, they allow the workflow to compare simpler linear formulations against richer nonlinear alternatives that may capture more complex similarity patterns among genotypes, phenomic profiles, and environments.

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 Final integrity check of the ETA list

Before moving to the validation design, it is useful to verify that the computational representation of the models is complete. This final check confirms that the number of ETA objects matches the number of models listed in the catalog. Keeping these two structures aligned is essential, because later steps assume that model labels, model numbers, and ETA specifications refer to the same ordered set of 18 models.

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

[1] 18

7. Cross-validation design

Prediction accuracy depends not only on the fitted model, but also on the prediction problem being imposed. In this workflow, the four validation schemes are implemented through two shared fits: one genotype-based fit that is later decomposed into CV1 and CV2, and one leave-one-environment-out fit that is later decomposed into CV0 and CV00. Because these scenarios are not equivalent, model performance must always be interpreted together with the exact masking rule used in the execution module.

This section clarifies what is hidden in each validation scheme and what scientific question each one is intended to answer. That distinction is essential before moving to the execution stage, because the same model may perform very differently depending on whether the challenge lies on the genotype axis, on the environment axis, or on both simultaneously.

7.1 Validation scenarios and their scientific meaning

The four validation schemes adopted in this study are summarized below. Each one corresponds to a different prediction target and therefore evaluates a different notion of generalization. Read this table as a map of the prediction questions that will later be implemented in the execution submodule.

cv_scenarios <- data.frame(
  CV = c("CV1", "CV2", "CV0", "CV00"),
  WhatIsMasked = c(
    "All observations from held-out genotypes",
    "No new masking; evaluation on training genotypes from the CV1 fit",
    "All observations from one held-out environment after genotype-based training masking",
    "Held-out genotypes inside one held-out environment under the same CV0 fit"
  ),
  MainQuestion = c(
    "Can the model predict completely new genotypes?",
    "How well does the same CV1 fit describe the genotypes that remained in training?",
    "Can the model predict previously seen genotypes in a completely held-out environment?",
    "Can the model predict new genotypes inside a completely held-out environment?"
  ),
  stringsAsFactors = FALSE
)

cv_scenarios

7.2 Prediction when genotypes are partially or completely unobserved

The first two schemes focus on genotype-level uncertainty, but they are produced from the same fit. In CV1, all observations from the held-out genotypes are removed before fitting and the evaluation is performed on those omitted genotypes. In CV2, no additional masking is introduced; instead, the same fit is evaluated on the genotypes that remained in training. This difference matters because CV1 measures extrapolation to unseen genotypes, whereas CV2 measures in-sample recovery under exactly the same fold split.

cv12_design <- data.frame(
  Scenario = c("CV1", "CV2"),
  TrainingSet = c(
    "All observations except those from held-out genotypes",
    "Exactly the same fitted model used for CV1"
  ),
  EvaluationTarget = c(
    "Predictions for held-out genotypes",
    "Predictions for the genotypes that remained in training"
  ),
  stringsAsFactors = FALSE
)

cv12_design

7.3 Prediction in environments that are not fully available during training

The schemes CV0 and CV00 shift the difficulty from the genotype axis to the environment axis, but they are also generated from the same fit. First, the training response keeps only the genotypes assigned to the training portion of the fold. Then, an entire environment is masked and the model is refit under that leave-one-environment-out rule. CV0 evaluates the genotypes that were already seen in other environments, whereas CV00 evaluates the genotypes that were held out by the fold split. This distinction is essential because the second target combines both genotype novelty and environment novelty.

cvloo_design <- data.frame(
  Scenario = c("CV0", "CV00"),
  TrainingSet = c(
    "Training genotypes only, with one environment fully masked",
    "Exactly the same fitted model used for CV0"
  ),
  EvaluationTarget = c(
    "Predictions for training genotypes inside the held-out environment",
    "Predictions for held-out genotypes inside the held-out environment"
  ),
  stringsAsFactors = FALSE
)

cvloo_design

7.4 File naming and downstream reproducibility

The prediction workflow now uses a hierarchical output layout so that each validation scenario, environment, trait, repetition, and fold can be inspected independently. Final CSV summaries are written inside output/results/ under scenario-specific folders, and the persistent BGLR fitting artifacts are written inside output/results/bglr_runs/ using the same logical structure. Keeping this pattern explicit is important because the next modules use these paths to inventory, summarize, and visualize the results.

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. Transition to the execution submodule

At this point, the prediction stage has been organized from three complementary perspectives: the analytical inputs are defined, the reduced catalog of 18 models is made explicit, and the validation schemes are linked to the scientific questions they are meant to answer. This means that the execution stage no longer needs to introduce new concepts. Its role is to apply the structure defined here, generate the prediction files, and organize the outputs that will later be summarized and visualized.

The next submodule, analysis_prediction_run_outputs.Rmd, therefore moves from design to implementation. There the emphasis shifts from model definition and validation logic to execution records, persistent BGLR artifacts organized by scenario, and recursive inventories that make the prediction workflow easier to inspect and reuse in the downstream modules.

Stage navigation

This module connects the structural definition of the prediction analysis to the execution-and-outputs stage that follows. Readers who want to review how the model set was constructed should remain in this file. Readers who want to inspect how the predictions are executed and organized should continue to the next module.

Previous stage: Variance components
Next stage: Prediction — Part 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     

other attached packages:
[1] workflowr_1.7.2

loaded via a namespace (and not attached):
 [1] vctrs_0.7.1       httr_1.4.7        cli_3.6.5         knitr_1.50       
 [5] rlang_1.1.7       xfun_0.54         stringi_1.8.7     otel_0.2.0       
 [9] processx_3.8.6    promises_1.5.0    jsonlite_2.0.0    glue_1.8.0       
[13] rprojroot_2.1.1   git2r_0.36.2      htmltools_0.5.9   httpuv_1.6.16    
[17] ps_1.9.1          sass_0.4.10       rmarkdown_2.30    jquerylib_0.1.4  
[21] tibble_3.3.1      evaluate_1.0.5    fastmap_1.2.0     yaml_2.3.12      
[25] lifecycle_1.0.5   whisker_0.4.1     stringr_1.6.0     compiler_4.5.1   
[29] fs_1.6.6          pkgconfig_2.0.3   Rcpp_1.1.1        rstudioapi_0.17.1
[33] later_1.4.4       digest_0.6.39     R6_2.6.1          pillar_1.11.1    
[37] callr_3.7.6       magrittr_2.0.4    bslib_0.9.0       tools_4.5.1      
[41] cachem_1.1.0      getPass_0.2-4