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inst/doc/tensorEVD-documentation.R
In tensorEVD: A Fast Algorithm to Factorize High-Dimensional Tensor Product Matrices
## ----initialsetup, include=FALSE----------------------------------------------
knitr::opts_chunk$set(cache=FALSE)
## ----eval=FALSE---------------------------------------------------------------
# library(data.table)
# setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
#
# PHENO <- read.csv("source/PHENO.csv")
# GENO <- fread("source/GENO.csv", data.table=FALSE)
# ECOV <- read.csv("source/ECOV.csv", row.names=1)
#
# # Select North region
# PHENO <- PHENO[PHENO$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")
## ----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")
## ----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
## ----eval=T-------------------------------------------------------------------
source("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/functions.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)
## ----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)
## ----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)
## ----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")
## ----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")
## ----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,"'")
# }
# }
## ----eval=TRUE----------------------------------------------------------------
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_ANOVA.txt"))
head(out)
## ----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$trait==trait,]
figure4 <- make_plot(dat, x='alpha', y='mean', SD="SD",
group="method", facet="source",
xlab=bquote(alpha~"x100% of variance of K"),
ylab=paste0("Proportion of variance of ",trait),
group.color=color2, scales="free_y",
ylabels=myfun, text=myfun, ylim=c(0,NA))
#print(figure4)
## ----eval=FALSE---------------------------------------------------------------
# setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
# load("data/pheno.RData")
# table(PHENO$CV_10fold)
# # 1 2 3 4 5 6 7 8 9 10
# #6180 6277 6246 5785 6160 5858 5492 5660 5436 5975
## ----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")
## ----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)
## ----eval=TRUE----------------------------------------------------------------
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_10F_CV.txt"))
head(out)
## ----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$tensorEVD))
if(requireNamespace("ggplot2", quietly=TRUE)){
figure5 <- ggplot2::ggplot(out, ggplot2::aes(tensorEVD, eigen)) +
ggplot2::geom_abline(color="gray70", linetype="dashed") +
ggplot2::geom_point(fill="#56B4E9", shape=21, size=1.4) +
ggplot2::facet_grid(trait ~ alpha) +
ggplot2::theme_bw() + ggplot2::xlim(rg) + ggplot2::ylim(rg)
}
#print(figure5)
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tensorEVD documentation built on Sept. 11, 2024, 9:02 p.m.
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## ----initialsetup, include=FALSE----------------------------------------------
knitr::opts_chunk$set(cache=FALSE)
## ----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$year_loc)]
# KE <- KE/mean(diag(KE))
#
# save(KE, file="data/ERM.RData")
## ----eval=FALSE---------------------------------------------------------------
# JOBS <- expand.grid(nG = c(100,500,1000),
# nE = c(10,30,50),
# n = c(10000,20000,30000),
# alpha = c(0.90,0.95,0.98),
# replicate = 1:10)
# dim(JOBS); head(JOBS)
# #[1] 810 5
# # nG nE n alpha replicate
# #1 100 10 10000 0.9 1
# #2 500 10 10000 0.9 1
# #3 1000 10 10000 0.9 1
# #4 100 30 10000 0.9 1
# #5 500 30 10000 0.9 1
# #6 1000 30 10000 0.9 1
#
# save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS1.RData")
## ----eval=TRUE----------------------------------------------------------------
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_simulation.txt"))
head(out[,1:7])
head(out[,8:12]) # results from the eigen function
head(out[,13:17]) # results from the tensorEVD function
## ----eval=T-------------------------------------------------------------------
source("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/functions.R")
## ----eval=T, fig.width = 7, fig.height = 5, fig.align='center'----------------
# Some data edits
out$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$n/1000
breaks0 <- seq(1,4,by=1)
figure1 <- make_plot(dat0, type="line", x='n', y='ratio', group="alpha",
group.label=NULL, facet="nG", facet2="nE", facet.type="grid",
xlab="Sample size (x1000)",
ylab="Computation time ratio (eigen/tensorEVD)",
group.color=color1, nSD=0, errorbar.size=0,
breaks.y=breaks0, labels.y=sprintf("%.f",10^breaks0),
scales="fixed")
#print(figure1)
## ----eval=T, fig.width = 7, fig.height = 5, fig.align='center'----------------
dat0 <- dat[dat$method %in% c("eigen","tensorEVD") & dat$alpha!="100",]
dat0$method <- factor(as.character(dat0$method))
dat0$alpha <- factor(as.character(dat0$alpha))
# Figure 2: Approximation accuracy using Frobenious norm
figure2 <- make_plot(dat0, x='alpha', y='Frobenius',
group="method", by="n", facet="nG", facet2="nE", facet.type="grid",
xlab=bquote(alpha~"x100% of variance of K"),
ylab=expression("Frobenius norm ("~abs(abs(K-hat(K)))[F]~")"),
by.label="Sample size", breaks.y=seq(0,500,by=100),
group.color=color2, rect.by.height=-0.05, ylim=c(0,NA))
#print(figure2)
## ----eval=T, fig.width = 7, fig.height = 5, fig.align='center'----------------
figure3 <- make_plot(dat0, x='alpha', y='pPC',
group="method", by="n", facet="nG", facet2="nE", facet.type="grid",
xlab=bquote(alpha~"x100% of variance of K"),
ylab="Number of eigenvectors/rank", by.label="Sample size",
group.color=color2, rect.by.height=-0.05,
hline=1, hline.color="red2", ylim=c(0,NA))
#print(figure3)
## ----eval=FALSE---------------------------------------------------------------
# JOBS <- expand.grid(alpha = c(0.90,0.95,0.98,1.00),
# replicate = 1:5)
# dim(JOBS); head(JOBS)
# #[1] 20 2
# # alpha replicate
# #1 0.90 1
# #2 0.95 1
# #3 0.98 1
# #4 1.00 1
# #5 0.90 2
# #6 0.95 2
#
# save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS2.RData")
## ----eval=FALSE---------------------------------------------------------------
# JOBS <- expand.grid(trait = c("yield","anthesis","silking","ASI"),
# method = c("eigen","tensorEVD"),
# alpha = c(0.90,0.95,0.98,1.00),
# replicate = 1:5)
# JOBS <- JOBS[-which(JOBS$alpha==1.00 & JOBS$method=="tensorEVD"),]
# dim(JOBS); head(JOBS)
# #[1] 140 4
# # trait method alpha replicate
# #1 yield eigen 0.9 1
# #2 anthesis eigen 0.9 1
# #3 silking eigen 0.9 1
# #4 ASI eigen 0.9 1
# #5 yield tensorEVD 0.9 1
# #6 anthesis tensorEVD 0.9 1
#
# save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS3.RData")
## ----eval=FALSE---------------------------------------------------------------
# setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
# load("parms/JOBS3.RData")
# prefix <- "output/genomic_prediction/ANOVA"
#
# out <- c()
# for(k in 1:nrow(JOBS))
# {
# trait <- as.vector(JOBS[k,"trait"])
# method <- as.vector(JOBS[k,"method"])
# alpha <- as.vector(JOBS[k,"alpha"])
# replicate <- as.vector(JOBS[k,"replicate"])
#
# suffix <- paste0(trait,"/",method,"/alpha_",100*alpha,"/rep_",replicate,"/VC.RData")
# filename <- paste0(prefix,"/",suffix)
# if(file.exists(filename)){
# load(filename)
# out <- rbind(out, VC)
# }else{
# message("File not found: '",suffix,"'")
# }
# }
## ----eval=TRUE----------------------------------------------------------------
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_ANOVA.txt"))
head(out)
## ----eval=T, fig.width = 6.5, fig.height = 4.5, fig.align='center'------------
out$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)
## ----eval=FALSE---------------------------------------------------------------
# setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
# load("data/pheno.RData")
# table(PHENO$CV_10fold)
# # 1 2 3 4 5 6 7 8 9 10
# #6180 6277 6246 5785 6160 5858 5492 5660 5436 5975
## ----eval=FALSE---------------------------------------------------------------
# JOBS <- expand.grid(trait = c("yield","anthesis","silking","ASI"),
# method = c("eigen","tensorEVD"),
# alpha = c(0.90,0.95,0.98),
# fold = 1:10)
# dim(JOBS); head(JOBS)
# #[1] 240 4
# # trait method alpha fold
# #1 yield eigen 0.9 1
# #2 anthesis eigen 0.9 1
# #3 silking eigen 0.9 1
# #4 ASI eigen 0.9 1
# #5 yield tensorEVD 0.9 1
# #6 anthesis tensorEVD 0.9 1
#
# save(JOBS, file="/mnt/scratch/quantgen/TENSOR_EVD/pipeline/parms/JOBS4.RData")
## ----eval=FALSE---------------------------------------------------------------
# setwd("/mnt/scratch/quantgen/TENSOR_EVD/pipeline")
# source("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/misc/functions.R")
# load("parms/JOBS4.RData")
# prefix <- "output/genomic_prediction/10F_CV"
#
# dat <- c()
# for(trait in levels(JOBS$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$method)]
# colnames(tmp) <- paste0(colnames(tmp),".SE")
# out <- data.frame(out, tmp)
## ----eval=TRUE----------------------------------------------------------------
out <- read.csv(url("https://raw.githubusercontent.com/MarcooLopez/tensorEVD/main/inst/extdata/results_10F_CV.txt"))
head(out)
## ----eval=T, fig.width = 6.0, fig.height = 6.5, fig.align='center'------------
out$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)
Try the tensorEVD package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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