---
title: "Documentation: A fast algorithm to factorize high-dimensional Tensor Product matrices used in Genetic Models"
author:
  - name: Marco Lopez-Cruz^1,$\star$^, Paulino Pérez-Rodríguez^2^, and Gustavo de los Campos^1,3,4^
    affiliation:
    - ^1^Department of Epidemiology and Biostatistics, Michigan State University, USA.
    - ^2^Socioeconomía, Estadística e Informática, Colegio de Postgraduados, México.
    - ^3^Department of Statistics and Probability, Michigan State University, USA.
    - ^4^Institute for Quantitative Health Science and Engineering, Michigan State University, USA.
    - ^$\star$^Corresponding author (<lopezcru@msu.edu>)
output:
  html_document:
    toc: true
    toc_float:
      collapsed: no
vignette: >
  %\VignetteIndexEntry{Documentation: A fast algorithm to factorize high-dimensional tensor product matrices}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
<style>
 body{
   background-color: white;
 }
 h1{
   color: maroon;
 }
 h2{
   color: #D2691E;
 }
 h3{
   color: #D2691E;
 }
 h4{
   color: #D2691E;
 }
 p{
   color: gray30;
 }
</style>

```{r initialsetup, include=FALSE}
knitr::opts_chunk$set(cache=FALSE)
```

## Benchmark

In this document we present details of the benchmark of the `tensorEVD()` routine against the `eigen()` function of the ‘base’ R-package (R Core Team 2021) in performing the eigenvalue decomposition (EVD) of a Hadamard matrix product involving two covariance structure matrices $\textbf{K}_1$ and $\textbf{K}_2$,

$$
\textbf{K} = (\textbf{Z}_1\textbf{K}_1\textbf{Z}'_1)\odot(\textbf{Z}_2\textbf{K}_2\textbf{Z}'_2).
$$
We assessed the computational time used to derive eigenvectors, the accuracy of the approximation provided by `tensorEVD()`, and the dimension of the resulting basis. Likewise, we evaluated the performance of the approximation of the Hadamard $\textbf{K}$ provided by the `tensorEVD()` method in Gaussian linear models in terms of variance components estimates and cross-validation prediction accuracies.

The data used in these benchmarks was generated by the Genomes-To-Fields (G2F) Initiative (Lima *et al*. 2023) which was curated and expanded by adding environmental covariates (EC) by Lopez-Cruz *et al*. (2023). We used the subset of the data corresponding to the northern testing locations that includes $n=59,069$ records for 4 traits (grain yield, anthesis, silking, and anthesis-silking interval) from $n_G = 4,344$ hybrids and $n_E = 97$ environments.

### Data analysis pipeline

We used a data analysis pipeline as shown below with folders `code`, `data`, `output`, `parms`, and `source`.

```{}
pipeline
├── code
│   ├── 1_simulation.R
│   ├── 2_model_components.R
│   ├── 3_ANOVA_GxE_model.R
│   ├── 4_get_variance_GxE_model.R
│   └── 5_10F_CV_GxE_model.R
├── data
├── output
├── parms
└── source
    ├── ECOV.csv
    ├── GENO.csv
    └── PHENO.csv
```

#### Genomes-to-Field data

Folder `source` contains the phenotypic (file `PHENO.csv`), SNPs (file `GENO.csv`), and ECs (file `ECOV.csv`) data from G2F. These files can be downloaded from the Figshare repository (https://doi.org/10.6084/m9.figshare.22776806).

#### R-scripts

Folder `code` contains the R-scripts to implement the sequence of analyses detailed in the next sections and can be downloaded from this [link](https://github.com/MarcooLopez/tensorEVD/tree/main/misc/code).

The R-scripts were run on the MSU high-performance computing center (HPCC) (https://docs.icer.msu.edu/Cluster_Resources/) as a batch job script. The header of the scripts contains the shebang line `#!/usr/bin/env Rscript` in the first line followed by job requirements (e.g., memory, number of CPUs, run time) that are specified using the `SLURM` scheduler by adding the prefix `#SBATCH` at the beginning of each request instruction line, for example

```{}
#!/usr/bin/env Rscript
#SBATCH --time=03:59:00
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=84G
#SBATCH --constraint=intel18
```

Each R-script is submitted to the HPCC using the `sbatch` command, for instance

```{}
cd /mnt/scratch/quantgen/TENSOR_EVD/pipeline/code
sbatch 1_simulation.R
```

### Data preparation

The R-code below show how to obtain the subset of the data corresponding to the northern locations. Next, this data subset is used to derive a genetic (GRM, VanRaden 2008) for the $n_G = 4,344$ hybrids as $\textbf{K}_G = \textbf{X}\textbf{X}'/trace(\textbf{X}\textbf{X}')$, where $\textbf{X}$ is the matrix of centered SNPs (hybrids in rows, SNPs in columns). Likewise, an environmental relationship matrix (ERM) is derived for the $n_E = 97$ environments as $\textbf{K}_E = \textbf{W}\textbf{W}'/trace(\textbf{W}\textbf{W}')$ where $\textbf{W}$ is the matrix of centered and scaled ECs (environments in rows, ECs in columns).

```{r eval=FALSE}
library(data.table)
setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")

PHENO <- read.csv("source/PHENO.csv")
GENO <- fread("source/GENO.csv", data.table=FALSE)
ECOV <- read.csv("source/ECOV.csv", row.names=1)

# Select North region
PHENO <- PHENO[PHENO$region %in% 'North',]
PHENO$year_loc <- factor(as.character(PHENO$year_loc))
PHENO$genotype <- factor(as.character(PHENO$genotype))

save(PHENO, file="data/pheno.RData")

# Calculate the GRM
ID <- GENO[,1]
GENO <- as.matrix(GENO[,-1])
rownames(GENO) <- ID
X <- scale(GENO, center=TRUE, scale=FALSE)
KG <- tcrossprod(X)
KG <- KG[levels(PHENO$genotype),levels(PHENO$genotype)]
KG <- KG/mean(diag(KG))

save(KG, file="data/GRM.RData")

# Calculate the ERM
ECOV <- ECOV[,-grep("HI30_",colnames(ECOV))]

KE <- tcrossprod(scale(ECOV))
KE <- KE[levels(PHENO$year_loc),levels(PHENO$year_loc)]
KE <- KE/mean(diag(KE))

save(KE, file="data/ERM.RData")
```

After running this code, R-files `pheno.RData`, `GRM.RData`, and `ERM.RData` with the phenotypic northern subset, GRM, and ERM, respectively, are to be saved in the folder `data`.

```{}
pipeline
:
├── data
:   ├── ERM.RData
    ├── GRM.RData
    └── pheno.RData
```

### Simulation experiments

We formed Hadamard products $\textbf{K} = (\textbf{Z}_1\textbf{K}_1\textbf{Z}'_1)\odot(\textbf{Z}_2\textbf{K}_2\textbf{Z}'_2)$ between the GRM (as $\textbf{K}_1$) and the ERM (as $\textbf{K}_2$) of various sizes by sampling hybrids ($n_G=100,500,1000$), environments ($n_E=10,30,50$), and the level of replication needed to complete a total sample size of $n=10000,20000,30000$. Then, we factorized the resulting Hadamard product matrix using the R-base function `eigen()` as well as using `tensorEVD()`, deriving as many eigenvectors as needed to explain a proportion $\alpha=0.90,0.95,0.98$ of the total variance. We implemented 10 replicates of each experiment.

The R-script [1_simulation.R](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/code/1_simulation.R) was used to perform the analysis for a given combination of parameters (`nG`, `nE`, `n`, ` alpha`, and `replicate`). To do this, first, we created an array with all combinations of the parameters. A `data.frame` object called `JOBS` containing $3\times3\times3\times3\times10=810$ rows and the $5$ parameters in columns, was created using the `expand.grid` R-function and saved in folder `parms`

```{r eval=FALSE}
JOBS <- expand.grid(nG = c(100,500,1000),
                    nE = c(10,30,50),
                    n = c(10000,20000,30000),
                    alpha = c(0.90,0.95,0.98),
                    replicate = 1:10)
dim(JOBS); head(JOBS)
#[1] 810   5
#    nG nE     n alpha replicate
#1  100 10 10000   0.9         1
#2  500 10 10000   0.9         1
#3 1000 10 10000   0.9         1
#4  100 30 10000   0.9         1
#5  500 30 10000   0.9         1
#6 1000 30 10000   0.9         1

save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS1.RData")
```

To perform all the $3\times3\times3\times3\times10=810$ cases (each row of the object `JOBS`) we submitted the R-script for multi-job implementation by specifying in the script header a job array through the `SLURM` option `#SBATCH --array=1-810`, each value of the array is read in the R-code with the instruction `Sys.getenv("SLURM_ARRAY_TASK_ID")`.

The output file `simulation_results.txt` generated after running the previous R-script contains at each row the results from each experiment case. The information of the experiment are given in the first columns followed by the results on computation time, approximation accuracy (measured with the Frobenius and CMD metrics), and the number of eigenvectors (associated to the $\alpha$-value) for the `eigen()` and `tensorEVD()` methods.

```{}
pipeline
:
├── output
:   └── simulation
        └── simulation_results.txt
```

The first rows of this file are shown below for the results presented in the manuscript (the file can be found in this [link](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_simulation.txt)).

```{r eval=TRUE}
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_simulation.txt"))
head(out[,1:7])
head(out[,8:12])   # results from the eigen function
head(out[,13:17])  # results from the tensorEVD function
```

#### Visualizing results

The following R-code chunks can be used to the reproduce Figures 1-3 that are presented in the manuscript. The code for the plots can be found in this [link](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/functions.R) and can be loaded into R using

```{r eval=T}
source("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/functions.R")
```

**Computation time**

R-code below creates a plot for the EVD computation time ratio (`eigen()`/`tensorEVD()`) (Figure 1).

```{r eval=T, fig.width = 7, fig.height = 5, fig.align='center'}
# Some data edits
out$alpha <- factor(100*out$alpha)

out$nG <- paste0("n[G]*' = '*",out$nG,"L")
out$nE <- paste0("n[E]*' = '*",out$nE,"L")
out$nG <- factor(out$nG, levels = unique(out$nG))
out$nE <- factor(out$nE, levels = unique(out$nE))

# Reshaping the data
measure <- c("time","Frobenius","CMD","nPC","pPC")
dat <- melt_data(out, id=c("nG","nE","n","alpha"),
                 measure=paste0(measure,"_"),
                 value.name=measure, variable.name="method")

color1 <- c('90%'="navajowhite2", '95%'="chocolate1", '98%'="red4")
color2 <- c(eigen="#E69F00", tensorEVD="#009E73", eigs="#56B4E9",
            trlan="#CC79A7", chol="#D55E00")

# Figure 1: Computation time ratio (eigen/tensorEVD)                  
dat0 <- out[out$alpha != "100",]
dat0$alpha <- factor(paste0(dat0$alpha,"%"))
dat0$ratio <- log10(dat0$time_eigen/dat0$time_tensorEVD)
dat0$n <- dat0$n/1000
breaks0 <- seq(1,4,by=1)

figure1 <- make_plot(dat0, type="line", x='n', y='ratio', group="alpha",
                     group.label=NULL, facet="nG", facet2="nE", facet.type="grid",
                     xlab="Sample size (x1000)",
                     ylab="Computation time ratio (eigen/tensorEVD)",
                     group.color=color1, nSD=0, errorbar.size=0,
                     breaks.y=breaks0, labels.y=sprintf("%.f",10^breaks0),
                     scales="fixed")
#print(figure1)
```

**Approximation accuracy**

R-code below produces the approximation accuracy plots using the Frobenius norm (Figure 2) of the difference between the Hadamard matrix and the approximation provided by `eigen()` and `tensorEVD()`.

```{r eval=T, fig.width = 7, fig.height = 5, fig.align='center'}
dat0 <- dat[dat$method %in% c("eigen","tensorEVD") & dat$alpha!="100",]
dat0$method <- factor(as.character(dat0$method))
dat0$alpha <- factor(as.character(dat0$alpha))

# Figure 2: Approximation accuracy using Frobenious norm
figure2 <- make_plot(dat0, x='alpha', y='Frobenius',
                     group="method", by="n", facet="nG", facet2="nE", facet.type="grid",
                     xlab=bquote(alpha~"x100% of variance of K"),
                     ylab=expression("Frobenius norm ("~abs(abs(K-hat(K)))[F]~")"),
                     by.label="Sample size", breaks.y=seq(0,500,by=100),
                     group.color=color2, rect.by.height=-0.05, ylim=c(0,NA))
#print(figure2)
```

**Dimension reduction**

The following R-code creates the plot (Figure 3) showing the number of eigenvectors produced by the `eigen()` and `tensorEVD()` methods, relative to the rank of the Hadamard matrix (number of eigenvectors with positive eigenvalue).

```{r eval=T, fig.width = 7, fig.height = 5, fig.align='center'}
figure3 <- make_plot(dat0, x='alpha', y='pPC',
                     group="method", by="n", facet="nG", facet2="nE", facet.type="grid",
                     xlab=bquote(alpha~"x100% of variance of K"),
                     ylab="Number of eigenvectors/rank", by.label="Sample size",
                     group.color=color2, rect.by.height=-0.05,
                     hline=1, hline.color="red2", ylim=c(0,NA))
#print(figure3)
```

### Application in Genomic Prediction
We analyzed each trait (grain yield, anthesis, silking, and anthesis-silking interval) with a Gaussian reaction norm $G\times E$ model (Jarquín *et al*. 2014) in which the trait phenotype ($y_{ijk}$) is modeled as the sum of the main effect of hybrid ($G_i$), main effect of environment ($E_j$), and the hybrid$\times$environment interaction ($GE_{ij}$) term, this is

$$
y_{ijk} = \mu + G_i + E_j + GE_{ij} + \varepsilon_{ijk}.   
$$

Above, $\mu$ is an intercept and $i$, $j$, and $k$ are indices for the hybrids, environment, and replicate, respectively. The term $\varepsilon_{ijk}$ is an error term assumed to be independently and identically Gaussian distributed as $\varepsilon_{ijk} \sim N(0,\sigma_{\varepsilon}^2)$, with $\sigma_{\varepsilon}^2$ variance parameter associated to the error. Hybrid, environment, and interaction effects were assumed to be multivariate normally distributed with zero mean and effect-specific covariance matrices, specifically $\textbf{G}\sim MVN(\textbf{0},\sigma_G^2 \textbf{K}_G)$, $\textbf{E}\sim MVN(\textbf{0},\sigma_E^2 \textbf{K}_E)$, and $\textbf{GE}\sim MVN(\textbf{0},\sigma_{GE}^2 \textbf{K})$, where

$$
\textbf{K} = (\textbf{Z}_1\textbf{K}_G\textbf{Z}'_1)\odot(\textbf{Z}_2\textbf{K}_E\textbf{Z}'_2)
$$

is a Hadamard product between the GRM $\textbf{K}_G$ and the ERM $\textbf{K}_E$, and $\sigma_G^2$, $\sigma_E^2$, and $\sigma_{GE}^2$ are variance parameters associated to $\textbf{G}$, $\textbf{E}$  and $\textbf{GE}$, respectively.

#### Bayesian Ridge Regression (BRR) implementation

We used a 'BRR' equivalence of the above model by fitting the model

$$
\boldsymbol{y} = \boldsymbol{\mu}+\textbf{X}_G\boldsymbol{\beta}_1+\textbf{X}_E\boldsymbol{\beta}_2 + \textbf{X}_{GE}\boldsymbol{\beta}_3 + \boldsymbol{\varepsilon}
$$

where the predictors $\textbf{X}_G$, $\textbf{X}_E$, and $\textbf{X}_{GE}$ are the scaled eigenvectors of the covariance matrices $\textbf{K}_G$, $\textbf{K}_E$, and $\textbf{K}$, respectively, and the regression coefficients are assumed to be distributed $\boldsymbol{\beta}_1\sim MVN(\textbf{0},\sigma_{G}^2 \textbf{I})$, $\boldsymbol{\beta}_2\sim MVN(\textbf{0},\sigma_{E}^2 \textbf{I})$, and $\boldsymbol{\beta}_3\sim MVN(\textbf{0},\sigma_{GE}^2 \textbf{I})$. First, we obtained the decomposition $\textbf{K}_G=\textbf{V}_1\textbf{D}_1\textbf{V}'_1$ and $\textbf{K}_E=\textbf{V}_2\textbf{D}_2\textbf{D}'_2$ using the *eigen* R-function. Next, for a given proportion $\alpha$ of variance, we obtained the decomposition of the Hadamard $\tilde{\textbf{K}}_{\alpha} = \tilde{\textbf{V}}_{\alpha}\tilde{\textbf{D}}_{\alpha}\tilde{\textbf{V}}_{\alpha}$ using the `eigen()` and `tensorEVD()` approaches. Finally, we derived the scaled eigenvectors $\textbf{X}_G = \textbf{V}_1\textbf{D}_1^{1/2}$, $\textbf{X}_E = \textbf{V}_2\textbf{D}_2^{1/2}$, and $\tilde{\textbf{X}}_{GE,\alpha} = \tilde{\textbf{V}}_{\alpha}\tilde{\textbf{D}}_{\alpha}^{1/2}$. The later was done for values $\alpha=0.90,0.95,0.98,1.00$.

The calculation of all these matrices was carried out using the R-script [2_model_components.R](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/code/2_model_components.R) for a given $\alpha$-value. We replicated this task 5 times to obtain an average (and SD) computing time of the decomposition, to this end, we created a job array for each combination of parameters `alpha` and `replicate` as follows

```{r eval=FALSE}
JOBS <- expand.grid(alpha = c(0.90,0.95,0.98,1.00),
                    replicate = 1:5)
dim(JOBS); head(JOBS)
#[1] 20  2
#  alpha replicate
#1  0.90         1
#2  0.95         1
#3  0.98         1
#4  1.00         1
#5  0.90         2
#6  0.95         2

save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS2.RData")
```

The script was submitted using a job array for the $4\times5=20$ combination of parameters (each row of the object `JOBS`) using the `SLURM` option `#SBATCH --array=1-20`. After running the R-script, the following files are created in the folder `output/genomic_prediction/model_comps`.

```{}
pipeline
:
├── output
:   └── genomic_prediction
        └── model_comps
            ├── XE.RData
            ├── XG.RData
            ├── XGE_90_eigen.RData
            ├── XGE_90_tensorEVD.RData
            ├── XGE_95_eigen.RData
            ├── XGE_95_tensorEVD.RData
            ├── XGE_98_eigen.RData
            ├── XGE_98_tensorEVD.RData
            ├── XGE_100_eigen.RData
            └── timing_EVD.txt
```

#### Analysis of variance

The $G\times E$ model was implemented as a BRR using the *BLRXy*() function from the 'BGLR' R-package (Pérez-Rodríguez and de los Campos 2022) for each combination of trait, method, and $\alpha$-value, with 5 replicates each to present an average (and SD) of the results. The model was run for $\alpha=1.0$ for the *eigen* method only. The *BLRXy*() function fits the model and generates samples from the posterior distribution of the regression coefficients $\boldsymbol{\beta}_1$, $\boldsymbol{\beta}_2$, and $\boldsymbol{\beta}_3$ using the Gibbs sampler. We run the BGLR with `nIter=50000` and `burnIn=5000` parameters.

We implemented the model using the R-script [3a_fit_GxE_model.R](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/code/3a_fit_full_GxE_model.R) for a given combination of parameters (`trait`, `method`, `alpha`, and `replicate`). We created an array with all combinations of parameters as follows

```{r eval=FALSE}
JOBS <- expand.grid(trait = c("yield","anthesis","silking","ASI"),
                    method = c("eigen","tensorEVD"),
                    alpha = c(0.90,0.95,0.98,1.00),
                    replicate = 1:5)
JOBS <- JOBS[-which(JOBS$alpha==1.00 & JOBS$method=="tensorEVD"),]
dim(JOBS); head(JOBS)
#[1] 140   4
#     trait    method alpha replicate
#1    yield     eigen   0.9         1
#2 anthesis     eigen   0.9         1
#3  silking     eigen   0.9         1
#4      ASI     eigen   0.9         1
#5    yield tensorEVD   0.9         1
#6 anthesis tensorEVD   0.9         1

save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS3.RData")
```

The script was submitted using a job array for all the $(4\times2\times4\times5) - (4\times1\times1\times5)=140$ combination of parameters (each row of object `JOBS`) using the `SLURM` option `#SBATCH --array=1-140`. The outputs generated by the R-script are saved in folder `output/genomic_prediction/ANOVA` in a sub-folder corresponding to each `trait`, `method`, `alpha`, and `replicate` combination as shown below. The posterior samples for coefficients $\boldsymbol{\beta}_1$, $\boldsymbol{\beta}_2$, and $\boldsymbol{\beta}_3$ are stored in files `ETA_G_b.bin`, `ETA_E_b.bin`, and `ETA_GE_b.bin`, respectively. For instance, the file `ETA_G_b.bin` is a $q\times p$ matrix containing at each row the samples $\hat{\boldsymbol{\beta}}_1^{(1)},\hat{\boldsymbol{\beta}}_1^{(2)},...,\hat{\boldsymbol{\beta}}_1^{(q)}$.

```{}
pipeline
:
├── output
:   └── genomic_prediction
        :
        └── ANOVA
            ├── yield
            :   ├── eigen
                :   ├── alpha_90
                    :   ├── rep_1
                        :   ├── ETA_E_b.bin
                            ├── ETA_E_varB.dat
                            ├── ETA_G_b.bin
                            ├── ETA_G_varB.dat
                            ├── ETA_GE_b.bin
                            ├── ETA_GE_varB.dat
                            ├── fm.RData
                            ├── mu.dat
                            └── varE.dat
```

**Obtaining total variance**

We used the sample files to obtain the total variance of hybrid ($\textbf{X}_G\boldsymbol{\beta}_1^{(k)}$), environment ($\textbf{X}_E\boldsymbol{\beta}_2^{(k)}$), hybrid$\times$environment interaction ($\textbf{X}_{GE}\boldsymbol{\beta}_3^{(k)}$), and error ($\boldsymbol{\varepsilon}$) terms in the BRR model, and reported the average across all samples. As we standardized the phenotype to have unit variance, these variances can be seen as the proportion of the phenotypic variance explained by each model component. We performed this task using the R-script [3b_get_variance_GxE_model.R](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/code/3b_get_variance_GxE_model.R) using a job array for all the $140$ jobs (each row in the object `JOBS`). The code will create a `data.frame` stored in the file `VC.RData` in the corresponding sub-folder in folder `output/genomic_prediction/ANOVA`.

**Collecting results**

Once the previous R-script has been run for all the jobs, the following code can be used to collect into a single table all the individual variance components results from all jobs.

```{r eval=FALSE}
setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
load("parms/JOBS3.RData")
prefix <- "output/genomic_prediction/ANOVA"

out <- c()
for(k in 1:nrow(JOBS))
{
  trait <- as.vector(JOBS[k,"trait"])
  method <- as.vector(JOBS[k,"method"])
  alpha <- as.vector(JOBS[k,"alpha"])
  replicate <- as.vector(JOBS[k,"replicate"])

  suffix <- paste0(trait,"/",method,"/alpha_",100*alpha,"/rep_",replicate,"/VC.RData")
  filename <- paste0(prefix,"/",suffix)
  if(file.exists(filename)){
    load(filename)
    out <- rbind(out, VC)
  }else{
    message("File not found: '",suffix,"'")
  }
}
```

The first rows of this `data.frame` are displayed below for the results presented in the manuscript (the file can be found in this [link](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_ANOVA.txt)).

```{r eval=TRUE}
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_ANOVA.txt"))
head(out)
```

**Visualizing results**

R-code below can be used to create Figure 4 in the manuscript showing the average proportion of phenotypic variance of grain yield explained by each model. The same code can be used for traits anthesis, silking, and anthesis-silking interval (Supplementary Figures 8, 9, and 10, respectively).

```{r eval=T, fig.width = 6.5, fig.height = 4.5, fig.align='center'}
out$alpha <- factor(paste0(100*out$alpha,"%"), levels=c("100%","98%","95%","90%"))
out$source <- factor(out$source, levels=c("G","E","GE","Error"))

trait <- c("yield", "anthesis", "silking", "ASI")[1]

myfun <- function(x) sprintf('%.3f', x)

# Figure 4: Phenotypic variance of yield
dat <- out[out$trait==trait,]
figure4 <- make_plot(dat, x='alpha', y='mean', SD="SD",
                     group="method", facet="source",
                     xlab=bquote(alpha~"x100% of variance of K"),
                     ylab=paste0("Proportion of variance of ",trait),
                     group.color=color2, scales="free_y",
                     ylabels=myfun, text=myfun, ylim=c(0,NA))
#print(figure4)
```

#### Cross-validation

We evaluated the performance of the approximation $\tilde{\textbf{K}}_{\alpha} = \tilde{\textbf{V}}_{\alpha}\tilde{\textbf{D}}_{\alpha}\tilde{\textbf{V}}_{\alpha}$ of the kernel $\textbf{K}$ provided by the `eigen()` and `tensorEVD()` methods in the $G\times E$ model in terms of prediction accuracy. We conducted a 10-fold cross-validation (CV) with hybrids assigned to folds. We predicted all the records of hybrids in the $k^{th}$ fold using a model trained with all records from hybrids in the remaining 9 folds. The model was implemented for each combination of trait and method for $\alpha=0.90,0.95,0.98$.

The folds were previously created by Lopez-Cruz *et al*. (2023) and are provided in the column `CV_10fold` of the phenotypic data file. The number records in each fold ranges between 5436 and 6277.

```{r eval=FALSE}
setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
load("data/pheno.RData")
table(PHENO$CV_10fold)
#   1    2    3    4    5    6    7    8    9   10
#6180 6277 6246 5785 6160 5858 5492 5660 5436 5975
```

We implemented this CV using the R-script [4_10F_CV_GxE_model.R](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/code/4_10F_CV_GxE_model.R) which fits the model for a given fold for a given combination of trait, method, and $\alpha$-value. To this end, we created an array with all combinations of parameters (`trait`, `method`, `alpha`, and `fold`) as follows

```{r eval=FALSE}
JOBS <- expand.grid(trait = c("yield","anthesis","silking","ASI"),
                    method = c("eigen","tensorEVD"),
                    alpha = c(0.90,0.95,0.98),
                    fold = 1:10)
dim(JOBS); head(JOBS)
#[1] 240   4
#     trait    method alpha fold
#1    yield     eigen   0.9    1
#2 anthesis     eigen   0.9    1
#3  silking     eigen   0.9    1
#4      ASI     eigen   0.9    1
#5    yield tensorEVD   0.9    1
#6 anthesis tensorEVD   0.9    1

save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS4.RData")
```

The script was submitted using a job array for all the $4\times2\times3\times10=240$ combination of parameters (each row of object `JOBS`) using the `SLURM` option `#SBATCH --array=1-240`. The outputs generated by the R-script are saved in folder `output/genomic_prediction/10F_CV` in a sub-folder corresponding to each `trait`, `method`, and `alpha` combination as shown below. Each file `results_fold_*.RData` contains a table with the predicted and observed values within each fold.

```{}
pipeline
:
├── output
:   └── genomic_prediction
        :
        └── 10F_CV
            ├── yield
            :   ├── eigen
                :   ├── alpha_90
                        ├── results_fold_1.RData
                        ├── results_fold_2.RData
                        ├── results_fold_3.RData
                        ├── results_fold_4.RData
                        ├── results_fold_5.RData
                        ├── results_fold_6.RData
                        ├── results_fold_7.RData
                        ├── results_fold_8.RData
                        ├── results_fold_9.RData
                        └── results_fold_10.RData
```


**Within-environment prediction accuracy**

The following R-code can be used to calculate the within environment (column `year_loc`) correlation between observed and predicted phenotypes. The code will collect first the results in files `results_fold_*.RData` for all folds from each job (a combination of `trait`, `method`, and `alpha`). The results from all jobs are to be saved in a single table.

```{r eval=FALSE}
setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
source("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/functions.R")
load("parms/JOBS4.RData")
prefix <- "output/genomic_prediction/10F_CV"

dat <- c()
for(trait in levels(JOBS$trait)){
  for(method in levels(JOBS$method)){
    for(alpha in unique(JOBS$alpha)){
      out0 <- c()
      for(fold in unique(JOBS$fold)){
        suffix <- paste0(trait,"/",method,"/alpha_",100*alpha,"/results_fold_",fold,".RData")
        filename <- paste0(prefix,"/",suffix)
        if(file.exists(filename)){
          load(filename)
          out0 <- rbind(out0, out)
        }else{
          message("File not found: '",suffix,"'")
        }
      }
      tmp <- get_corr(out0, by="year_loc")
      dat <- rbind(dat, data.frame(trait,method,alpha,tmp))
    }
  }
}

# Reshaping the data
dat$trait <- factor(dat$trait, levels=levels(JOBS$trait))
out <- reshape2::dcast(dat, trait+alpha+year_loc+nRecords~method, value.var="correlation")
tmp <- reshape2::dcast(dat, trait+alpha+year_loc+nRecords~method, value.var="SE")[,levels(JOBS$method)]
colnames(tmp) <- paste0(colnames(tmp),".SE")
out <- data.frame(out, tmp)
```

The first rows of this table are displayed below for the results presented in the manuscript (the file can be found in this [link](https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_10F_CV.txt)).

```{r eval=TRUE}
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_10F_CV.txt"))
head(out)
```

**Visualizing results**

R-code below can be used to create Figure 5 in the manuscript showing the within environment prediction correlation using the `tensorEVD()` and `eigen()` methods for each combination of trait and $\alpha$-value.

```{r eval=T, fig.width = 6.0, fig.height = 6.5, fig.align='center'}
out$trait <- factor(out$trait, levels=unique(out$trait))
out$alpha <- factor(paste0(100*out$alpha,"%"), levels=c("98%","95%","90%"))

# Figure 5: Within environment prediction correlation
rg <- range(c(out$eigen,out$tensorEVD))
if(requireNamespace("ggplot2", quietly=TRUE)){
  figure5 <-  ggplot2::ggplot(out, ggplot2::aes(tensorEVD, eigen)) +
              ggplot2::geom_abline(color="gray70", linetype="dashed") +
              ggplot2::geom_point(fill="#56B4E9", shape=21, size=1.4) +
              ggplot2::facet_grid(trait ~ alpha) +
              ggplot2::theme_bw() + ggplot2::xlim(rg) + ggplot2::ylim(rg)
}

#print(figure5)
```

## References

Henderson C. R., 1985 Best linear unbiased prediction of nonadditive genetic merits in noninbred populations. J. Anim. Sci. 60: 111–117.

Jarquín D., J. Crossa, X. Lacaze, P. Du Cheyron, J. Daucourt, et al., 2014 A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor. Appl. Genet. 127: 595–607.

Lima D. C., J. D. Washburn, J. I. Varela, Q. Chen, J. L. Gage, et al., 2023 Genomes to Fields 2022 Maize genotype by Environment Prediction Competition. BMC Res. Notes 16.

Lopez-Cruz M., F. Aguate, J. Washburn, S. K. Dayane, C. Lima, et al., 2023 Leveraging Data from the Genomes to Fields Initiative to Investigate G×E in Maize in North America. Nat. Comm. (in press)

Perez-Rodriguez P., and G. de los Campos, 2022 Additions to the BGLR R-package: a new function for biobank size data and Bayesian multivariate models, pp. 1486–1489 in *Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)*, Rotterdam.

R Core Team, 2021 *R: A Language and environment for statistical computing*. R Foundation for Statistical Computing, Vienna, Austria.
