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Script.R
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rm(list = ls())
# Library -----------------------------------------------------------------
library(tidyverse)
source('Supporting/crossvalid.R')
library(ggpubr)
library(ggtext)
library(FactoMineR)
library(ComplexHeatmap)
library(circlize)
library(parallel)
library(doParallel)
library(foreach)
library(BGLR)
library(ggnewscale)
library(gghighlight)
library(EnvRtype)
# Data --------------------------------------------------------------------
pheno = readRDS('Data/pheno.rds')
geno = load('Data/geno.rda')
# Diversity ---------------------------------------------------------------
G = tcrossprod(apply(genoF, 2, scale))/ncol(genoF)
rownames(G) = colnames(G) = rownames(genoF)
pca = PCA(G, scale.unit = F)
data.frame(
line = names(pca$ind$coord[,1]),
pc1 = pca$ind$coord[,1],
pc2 = pca$ind$coord[,2],
cat = ifelse(rownames(pca$ind$coord) %in% unique(pheno$Aline), 'A', 'R'),
row.names = NULL
) %>% ggplot(aes(x = pc1, y = pc2, color = cat, shape = cat)) +
geom_point(size = 2) +
labs(x = paste0('PC1 (', round(pca$eig[1,2],2), '%)'),
y = paste0('PC2 (', round(pca$eig[2,2],2), '%)'),
color = 'Parental lines', shape = 'Parental lines',
fill = 'Parental lines') +
theme(legend.position = 'top', legend.text = element_text(face = 'bold'),
legend.title = element_text(face = 'bold')) +
scale_colour_manual(values = c('#1CB80F','#386cb0')) +
scale_fill_manual(values = c('#1CB80F','#386cb0')) #+ ylim(-6,7.5) + xlim(-6,7.5)
Heatmap(
G[order(rownames(G)), order(colnames(G))],
column_order = sort(colnames(G)), row_order = sort(rownames(G)),
#col = colorRamp2(c(-.5,0,2), c('#5e3c99', '#f7f7f7', '#e66101')),
row_split = c(rep("A lines", length(unique(pheno$Aline))),
rep("R Lines", length(unique(pheno$Rline)))),
column_split = c(rep("A lines", length(unique(pheno$Aline))),
rep("R Lines", length(unique(pheno$Rline)))),
heatmap_legend_param = list(title = 'Kin', legend_height = unit(5, 'cm'),
border = 'black'),
row_names_gp = gpar(fontsize = 9), column_names_gp = gpar(fontsize = 9)
)
rm(pca, G, genoF)
# Genomic kinship matrices -----------------------------------------------
GA = tcrossprod(apply(genoA, 2, scale))/ncol(genoA)
rownames(GA) = colnames(GA) = rownames(genoA)
GA = GA[order(rownames(GA)), order(colnames(GA))]
GR = tcrossprod(apply(genoR, 2, scale))/ncol(genoR)
rownames(GR) = colnames(GR) = rownames(genoR)
GR = GR[order(rownames(GR)), order(colnames(GR))]
rm(genoA, genoR, genoF)
# Environmental data ------------------------------------------------------
envcov = read.csv('Data/envcov.csv')
id.vars = names(envcov)[-which(names(envcov) %in%
c('env','YEAR', 'MM','DD','DOY','YYYYMMDD',
'daysFromStart','soc','silt','sand','phh2o',
'bdod','alt','clay','LON','LAT','nit'))]
## Omega matrix: mean ------------------------------------------------------
W_mean = W_matrix(env.data = envcov, env.id = 'env', var.id = id.vars)
W_mean = cbind(W_mean, scale(unique(envcov[,c('env', 'soc', 'silt', 'sand',
'phh2o', 'bdod', 'alt', 'clay',
'LON', 'LAT', 'nit')])[,-1]))
O_mean = tcrossprod(W_mean)/ncol(W_mean)
## Omega matrix: 4 stages ------------------------------------------------------
W_stages = EnvRtype::W_matrix(env.data = envcov, env.id = 'env', var.id = id.vars,
by.interval = T, time.window = c(20,60,95,130),
names.window = c('E0_E1','E2_E5','E6_E7','E8_E9'))
W_stages = W_stages[,-grep('NA', colnames(W_stages))]
W_stages = cbind(W_stages, scale(unique(envcov[,c('env', 'soc', 'silt', 'sand',
'phh2o', 'bdod', 'alt', 'clay',
'LON', 'LAT', 'nit')])[,-1]))
O_stages = tcrossprod(W_stages)/ncol(W_stages)
## Omega matrix: daily ------------------------------------------------------
W_daily = EnvRtype::W_matrix(env.data = envcov, env.id = 'env', var.id = id.vars,
by.interval = T, time.window = 1:130)
W_daily = cbind(W_daily, scale(unique(envcov[,c('env', 'soc', 'silt', 'sand',
'phh2o', 'bdod', 'alt', 'clay',
'LON', 'LAT', 'nit')])[,-1]))
O_daily = tcrossprod(W_daily)/ncol(W_daily)
## Critical time window ------------------------------------------------------
pb = winProgressBar(
min = 0, max = length(unique(envcov$daysFromStart)),
label = 'Completed', width = 300L, initial = 0,
title = 'Critical time window'
)
corr = list()
fit = list()
for (i in 1:length(unique(envcov$daysFromStart))) {
for (j in 1:length(unique(envcov$daysFromStart))) {
if (length(unique(envcov$daysFromStart)) - i < 6) break
if (j-i < 6) next
test = list()
for (k in unique(pheno$env)) {
test[[k]] = apply(
as.matrix(envcov[
which(envcov$daysFromStart %in% i:j & envcov$env == k),
-which(names(envcov) %in%
c('env','YEAR', 'MM','DD','DOY','YYYYMMDD',
'daysFromStart', 'alt', 'LON', 'LAT',
'soc','silt','sand','phh2o','nit','clay','bdod'))
]), 2, mean
)
}
test = cbind(do.call(rbind,test)[order(rownames(do.call(rbind,test))),],
y = tapply(pheno$EBLUE, pheno$env, mean))
correl = as.matrix(cor(test)[,'y'])
R2 = as.matrix(apply(test[,-which(colnames(test) == 'y')], 2, function(x){
summary(lm(test[,'y'] ~ x))$r.squared}))
colnames(correl) = colnames(R2) = paste(i, j, sep = '-')
corr[[paste(i, j, sep = '-')]] = correl
fit[[paste(i, j, sep = '-')]] = R2
rm(test, correl, R2)
}
pctg = paste0(round(i/length(unique(envcov$daysFromStart)) * 100, 0), '% done')
setWinProgressBar(pb, i, label = pctg)
}
close(pb)
cpt.corr = do.call(cbind, corr)
cpt.corr = as.matrix(apply(cpt.corr[-which(rownames(cpt.corr) == 'y'),], 1,
function(x) names(which.max(x))))
cpt.fit = do.call(cbind, fit)
cpt.fit = as.matrix(apply(cpt.fit, 1, function(x) names(which.max(x))))
## Leave-One-Out cross-validation
pb = winProgressBar(
min = 0, max = length(unique(pheno$env)),
label = 'Progress', width = 300L, initial = 0,
title = 'Critical time window - LOO'
)
results.fit = list()
results.corr = list()
for (f in unique(pheno$env)) {
loo = envcov[-which(envcov$env == f),]
means = tapply(pheno[-which(pheno$env == f),'EBLUE'],
pheno[-which(pheno$env == f),'env'],
mean)
corr = list()
fit = list()
for (i in 1:length(unique(envcov$daysFromStart))) {
for (j in 1:length(unique(envcov$daysFromStart))) {
if (length(unique(envcov$daysFromStart)) - i < 6) break
if (j-i < 6) next
test = list()
for (k in unique(pheno$env)[-which(unique(pheno$env) == f)]) {
test[[k]] = apply(
as.matrix(loo[
which(loo$daysFromStart %in% i:j & loo$env == k),
-which(names(loo) %in%
c('env','YEAR', 'MM','DD','DOY','YYYYMMDD',
'daysFromStart', 'alt', 'LON', 'LAT',
'soc','silt','sand','phh2o','nit','clay','bdod'))
]), 2, mean
)
}
test = cbind(do.call(rbind,test)[order(rownames(do.call(rbind,test))),],
y = means)
correl = as.matrix(cor(test)[,'y'])
R2 = as.matrix(apply(test[,-which(colnames(test) == 'y')], 2, function(x){
summary(lm(test[,'y'] ~ x))$r.squared}))
colnames(correl) = colnames(R2) = paste(i, j, sep = '-')
corr[[paste(i, j, sep = '-')]] = correl
fit[[paste(i, j, sep = '-')]] = R2
rm(test,correl)
}
}
results.corr[[f]] = do.call(cbind, corr)
results.fit[[f]] = do.call(cbind, fit)
rm(corr, fit)
pctg = paste0(round(which(unique(pheno$env) == f)/length(unique(pheno$env)) * 100, 0), '% done')
setWinProgressBar(pb, f, label = pctg)
}
close(pb)
cpt_loo.corr = do.call(cbind, lapply(results.corr, function(x){
as.matrix(apply(x[-which(rownames(x) == 'y'),], 1,
function(y) names(which.max(y))))
}))
cpt_loo.fit = do.call(cbind, lapply(results.fit, function(x){
as.matrix(apply(x, 1, function(y) names(which.max(y))))
}))
ctw.corr = as.matrix(apply(cbind(cpt_loo.corr, cpt.corr), 1,
function(x) names(which.max(table(x)))))
ctw.fit = as.matrix(apply(cbind(cpt_loo.fit, cpt.fit), 1,
function(x) names(which.max(table(x)))))
ctw = cbind(corr = ctw.corr, R2 = ctw.fit)
cpt = data.frame(
var = rownames(ctw),
start = as.numeric(sub('-', '',str_extract(ctw[,1], pattern = '\\d+\\-'))),
end = as.numeric(sub('-', '',str_extract(ctw[,1], pattern = '\\-\\d+')))
)
## Optimized omega matrix: mean ------------------------------------------------------
W_opt_mean = matrix(NA, nrow = length(unique(envcov$env)),
ncol = length(unique(cpt$var)),
dimnames = list(unique(envcov$env), unique(cpt$var)))
for (i in 1:nrow(cpt)) {
W_opt_mean[,i] = tapply(
as.matrix(envcov[which(envcov$daysFromStart >= cpt[i, 'start'] &
envcov$daysFromStart <= cpt[i, 'end']),
c('env','daysFromStart',cpt[i, 'var'])][,cpt[i, 'var']]),
as.matrix(envcov[which(envcov$daysFromStart >= cpt[i, 'start'] &
envcov$daysFromStart <= cpt[i, 'end']),
c('env','daysFromStart',cpt[i, 'var'])][,'env']),
mean
)
}
W_opt_mean = as.matrix(cbind(W_opt_mean, unique(envcov[
,c('env','LON','LAT','alt','soc','silt','sand','phh2o','nit','clay','bdod')
])[,c('LON','LAT','alt','soc','silt','sand','phh2o','nit','clay','bdod')]))
W_opt_mean = apply(W_opt_mean, 2, scale)
O_opt_mean = tcrossprod(W_opt_mean)/ncol(W_opt_mean)
## Most influential environmental covariates --------------------------------
colnames(W_mean) = c('iihs', "hiri", 'etp', 'frue', 'gdd', 'dh', 'sh', 'petp', 'prec',
'rh', 'rta', 'spv', 'tmax', 'tmean', 'tmin', 'trange', 'tdew',
'vpd', 'ws', 'soc', 'silt', 'sand', 'phh2o', 'bdod', 'alt', 'clay',
'lon', 'lat', 'nit')
blupsgei = predict(modgei, classify = 'gen:env', pworkspace = '2gb')$pvals
blupsgei = left_join(blupsgei %>% select(-std.error, -status),
as.data.frame(W_mean) %>% rownames_to_column('env'), by = 'env')
d = lm(
formula = reformulate(paste(colnames(W_mean)[-c(15,16,9)], collapse = '+'),
response = 'predicted.value'),
data = blupsgei
)
summary(d)
GEIrel = relaimpo::calc.relimp(d, type = 'lmg', rela = F, rank = T)
GEIrel.df = data.frame(GEIrel@lmg) %>% rownames_to_column('covamb') %>%
mutate(beta = scale(coef(d))[-1])
ggarrange(
GEIrel.df %>% ggplot(aes(x = reorder(covamb,beta), y = beta, fill = beta)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90), legend.position = 'none') +
labs(x = '', y = 'Regression coefficient')+
scale_fill_viridis_c(option = 'D'),
GEIrel.df %>% ggplot(aes(x = reorder(covamb,GEIrel.lmg),
y = (GEIrel.lmg/GEIrel@R2.decomp)*100,
fill = (GEIrel.lmg/GEIrel@R2.decomp)*100)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90), legend.position = 'none') +
labs(x = 'Environmental covariates', y = 'Part in R² (%)',
caption = paste('R² =', round(GEIrel@R2.decomp, 2))) +
scale_fill_viridis_c(option = 'H'),
nrow = 2,labels = 'auto'
)
# GP Models ---------------------------------------------------------------
## Design matrices ------------------------------------------------------
ZA = model.matrix(~-1 + Aline, data = pheno)
ZR = model.matrix(~-1 + Rline, data = pheno)
ZE = model.matrix(~-1 + env, data = pheno)
ZH = model.matrix(~-1 + gen, data = pheno)
rownames(ZA) = rownames(ZR) = rownames(ZE) = rownames(ZH) = pheno$gen
## Connectivity ------------------------------------------------------------
con = crossprod(ZE,ZH)
colnames(con) = sub('gen','',colnames(con))
rownames(con) = sub('env','',rownames(con))
con = ifelse(con == 1, 'P', 'A')
Heatmap(
con, col = c('white', '#386cb0'),
show_heatmap_legend = F,
row_names_gp = gpar(fontsize = 7),
show_column_names = F, show_row_names = F,
column_title = 'Hybrids', row_title = 'Environments'
)
table(con)['P']/sum(table(con)) * 100
rm(con)
## Full covariances matrices ------------------------------------------------
ZGZ_A = tcrossprod(tcrossprod(ZA, GA),ZA)
ZGZ_R = tcrossprod(tcrossprod(ZR, GR),ZR)
ZGZ_H = ZGZ_A * ZGZ_R
ZZ_E = tcrossprod(ZE)
GEI_A = ZGZ_A * ZZ_E
GEI_R = ZGZ_R * ZZ_E
GEI_H = ZGZ_H * ZZ_E
ZOZ_E_mean = tcrossprod(tcrossprod(ZE, O_mean), ZE)
GEIO_A_mean = ZGZ_A * ZOZ_E_mean
GEIO_R_mean = ZGZ_R * ZOZ_E_mean
GEIO_H_mean = ZGZ_H * ZOZ_E_mean
ZOZ_E_stages = tcrossprod(tcrossprod(ZE, O_stages), ZE)
GEIO_A_stages = ZGZ_A * ZOZ_E_stages
GEIO_R_stages = ZGZ_R * ZOZ_E_stages
GEIO_H_stages = ZGZ_H * ZOZ_E_stages
ZOZ_E_daily = tcrossprod(tcrossprod(ZE, O_daily), ZE)
GEIO_A_daily = ZGZ_A * ZOZ_E_daily
GEIO_R_daily = ZGZ_R * ZOZ_E_daily
GEIO_H_daily = ZGZ_H * ZOZ_E_daily
ZOZ_E_opt_daily = tcrossprod(tcrossprod(ZE, O_opt_daily), ZE)
GEIO_A_opt_daily = ZGZ_A * ZOZ_E_opt_daily
GEIO_R_opt_daily = ZGZ_R * ZOZ_E_opt_daily
GEIO_H_opt_daily = ZGZ_H * ZOZ_E_opt_daily
ZOZ_E_opt_mean = tcrossprod(tcrossprod(ZE, O_opt_mean), ZE)
GEIO_A_opt_mean = ZGZ_A * ZOZ_E_opt_mean
GEIO_R_opt_mean = ZGZ_R * ZOZ_E_opt_mean
GEIO_H_opt_mean = ZGZ_H * ZOZ_E_opt_mean
## Model 1 - Only GCA -------------------------------------
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZGZ_A)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS')
)
M1 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m1 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M1$yHat
)
varcomp_m1 = data.frame(
effect = c("E", 'Aline', 'Rline', 'e'),
varcomp = c(M1$ETA[[1]]$varU, M1$ETA[[2]]$varU, M1$ETA[[3]]$varU, M1$varE),
perc = c(M1$ETA[[1]]$varU/sum(c(M1$ETA[[1]]$varU, M1$ETA[[2]]$varU, M1$ETA[[3]]$varU, M1$varE)),
M1$ETA[[2]]$varU/sum(c(M1$ETA[[1]]$varU, M1$ETA[[2]]$varU, M1$ETA[[3]]$varU, M1$varE)),
M1$ETA[[3]]$varU/sum(c(M1$ETA[[1]]$varU, M1$ETA[[2]]$varU, M1$ETA[[3]]$varU, M1$varE)),
M1$varE/sum(c(M1$ETA[[1]]$varU, M1$ETA[[2]]$varU, M1$ETA[[3]]$varU, M1$varE)))
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M1_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M1_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M1_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M1_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 2 - GCA and SCA --------------------------
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS')
)
M2 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m2 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M2$yHat
)
varcomp_m2 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid','e'),
varcomp = c(
M2$ETA[[1]]$varU, M2$ETA[[2]]$varU, M2$ETA[[3]]$varU, M2$ETA[[4]]$varU,
M2$varE
),
perc = c(
M2$ETA[[1]]$varU/sum(c(M2$ETA[[1]]$varU, M2$ETA[[2]]$varU, M2$ETA[[3]]$varU,
M2$ETA[[4]]$varU, M2$varE)),
M2$ETA[[2]]$varU/sum(c(M2$ETA[[1]]$varU, M2$ETA[[2]]$varU, M2$ETA[[3]]$varU,
M2$ETA[[4]]$varU, M2$varE)),
M2$ETA[[3]]$varU/sum(c(M2$ETA[[1]]$varU, M2$ETA[[2]]$varU, M2$ETA[[3]]$varU,
M2$ETA[[4]]$varU, M2$varE)),
M2$ETA[[4]]$varU/sum(c(M2$ETA[[1]]$varU, M2$ETA[[2]]$varU, M2$ETA[[3]]$varU,
M2$ETA[[4]]$varU, M2$varE)),
M2$varE/sum(c(M2$ETA[[1]]$varU, M2$ETA[[2]]$varU, M2$ETA[[3]]$varU,
M2$ETA[[4]]$varU, M2$varE))
)
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M2_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M2_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M2_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M2_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 3 - GCA, SCA and Interactions -------------
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS'),
AE = list(V = eigen(GEI_A)$vectors, d = eigen(GEI_A)$values, model = 'RKHS'),
RE = list(V = eigen(GEI_R)$vectors, d = eigen(GEI_A)$values, model = 'RKHS'),
HE = list(V = eigen(GEI_H)$vectors, d = eigen(GEI_A)$values, model = 'RKHS')
)
M3 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m3 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M3$yHat
)
varcomp_m3 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid', 'AxE', 'RxE', 'HxE','e'),
varcomp = c(
M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU, M3$ETA[[4]]$varU,
M3$ETA[[5]]$varU, M3$ETA[[6]]$varU, M3$ETA[[7]]$varU, M3$varE
),
perc = c(
M3$ETA[[1]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$ETA[[2]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$ETA[[3]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$ETA[[4]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$ETA[[5]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$ETA[[6]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$ETA[[7]]$varU/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE)),
M3$varE/sum(c(M3$ETA[[1]]$varU, M3$ETA[[2]]$varU, M3$ETA[[3]]$varU,
M3$ETA[[4]]$varU, M3$ETA[[5]]$varU, M3$ETA[[6]]$varU,
M3$ETA[[7]]$varU, M3$varE))
)
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M3_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M3_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M3_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M3_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 4.1 - GCA, SCA and Interactions w/ EnvCov - mean -------------
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS'),
AW = list(V = eigen(GEIO_A_mean)$vectors, d = eigen(GEIO_A_mean)$values,
model = 'RKHS'),
RW = list(V = eigen(GEIO_R_mean)$vectors, d = eigen(GEIO_R_mean)$values,
model = 'RKHS'),
HW = list(V = eigen(GEIO_H_mean)$vectors, d = eigen(GEIO_H_mean)$values,
model = 'RKHS')
)
M4.1 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m4.1 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M4.1$yHat
)
varcomp_m4.1 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid', 'AxW', 'RxW', 'HxW','e'),
varcomp = c(
M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU, M4.1$ETA[[4]]$varU,
M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU, M4.1$ETA[[7]]$varU, M4.1$varE
),
perc = c(
M4.1$ETA[[1]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$ETA[[2]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$ETA[[3]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$ETA[[4]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$ETA[[5]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$ETA[[6]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$ETA[[7]]$varU/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE)),
M4.1$varE/sum(c(M4.1$ETA[[1]]$varU, M4.1$ETA[[2]]$varU, M4.1$ETA[[3]]$varU,
M4.1$ETA[[4]]$varU, M4.1$ETA[[5]]$varU, M4.1$ETA[[6]]$varU,
M4.1$ETA[[7]]$varU, M4.1$varE))
)
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M4.1_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M4.1_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M4.1_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M4.1_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 4.2 - GCA, SCA and Interactions w/ EnvCov - 4 stages -------------
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS'),
AW = list(V = eigen(GEIO_A_stages)$vectors, d = eigen(GEIO_A_stages)$values,
model = 'RKHS'),
RW = list(V = eigen(GEIO_R_stages)$vectors, d = eigen(GEIO_R_stages)$values,
model = 'RKHS'),
HW = list(V = eigen(GEIO_H_stages)$vectors, d = eigen(GEIO_H_stages)$values,
model = 'RKHS')
)
M4.2 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m4.2 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M4.2$yHat
)
varcomp_m4.2 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid', 'AxW', 'RxW', 'HxW','e'),
varcomp = c(
M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU, M4.2$ETA[[4]]$varU,
M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU, M4.2$ETA[[7]]$varU, M4.2$varE
),
perc = c(
M4.2$ETA[[1]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$ETA[[2]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$ETA[[3]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$ETA[[4]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$ETA[[5]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$ETA[[6]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$ETA[[7]]$varU/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE)),
M4.2$varE/sum(c(M4.2$ETA[[1]]$varU, M4.2$ETA[[2]]$varU, M4.2$ETA[[3]]$varU,
M4.2$ETA[[4]]$varU, M4.2$ETA[[5]]$varU, M4.2$ETA[[6]]$varU,
M4.2$ETA[[7]]$varU, M4.2$varE))
)
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M4.2_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M4.2_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M4.2_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M4.2_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 4.3 - GCA, SCA and Interactions w/ EnvCov - daily -------------
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS'),
AW = list(V = eigen(GEIO_A_daily)$vectors, d = eigen(GEIO_A_daily)$values,
model = 'RKHS'),
RW = list(V = eigen(GEIO_R_daily)$vectors, d = eigen(GEIO_R_daily)$values,
model = 'RKHS'),
HW = list(V = eigen(GEIO_H_daily)$vectors, d = eigen(GEIO_H_daily)$values,
model = 'RKHS')
)
M4.3 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m4.3 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M4.3$yHat
)
varcomp_m4.3 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid', 'AxW', 'RxW', 'HxW','e'),
varcomp = c(
M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU, M4.3$ETA[[4]]$varU,
M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU, M4.3$ETA[[7]]$varU, M4.3$varE
),
perc = c(
M4.3$ETA[[1]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$ETA[[2]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$ETA[[3]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$ETA[[4]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$ETA[[5]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$ETA[[6]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$ETA[[7]]$varU/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE)),
M4.3$varE/sum(c(M4.3$ETA[[1]]$varU, M4.3$ETA[[2]]$varU, M4.3$ETA[[3]]$varU,
M4.3$ETA[[4]]$varU, M4.3$ETA[[5]]$varU, M4.3$ETA[[6]]$varU,
M4.3$ETA[[7]]$varU, M4.3$varE))
)
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M4.3_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M4.3_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M4.3_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M4.3_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 5.1 - GCA, SCA and Interactions w/ EnvCov and without Envcov - mean ----
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS'),
AW = list(V = eigen(GEIO_A_mean)$vectors, d = eigen(GEIO_A_mean)$values,
model = 'RKHS'),
RW = list(V = eigen(GEIO_R_mean)$vectors, d = eigen(GEIO_R_mean)$values,
model = 'RKHS'),
HW = list(V = eigen(GEIO_H_mean)$vectors, d = eigen(GEIO_H_mean)$values,
model = 'RKHS'),
AE = list(V = eigen(GEI_A)$vectors, d = eigen(GEI_A)$values, model = 'RKHS'),
RE = list(V = eigen(GEI_R)$vectors, d = eigen(GEI_R)$values, model = 'RKHS'),
HE = list(V = eigen(GEI_H)$vectors, d = eigen(GEI_R)$values, model = 'RKHS')
)
M5.1 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m5.1 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M5.1$yHat
)
varcomp_m5.1 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid', 'AxW', 'RxW', 'HxW',
'AxE', 'RxE', 'HxE','e'),
varcomp = c(
M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU, M5.1$ETA[[4]]$varU,
M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU, M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU,
M5.1$ETA[[9]]$varU, M5.1$ETA[[10]]$varU, M5.1$varE
),
perc = c(
M5.1$ETA[[1]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[2]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[3]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[4]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[5]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[6]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[7]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[8]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[9]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$ETA[[10]]$varU/sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)),
M5.1$varE/sum(sum(c(M5.1$ETA[[1]]$varU, M5.1$ETA[[2]]$varU, M5.1$ETA[[3]]$varU,
M5.1$ETA[[4]]$varU, M5.1$ETA[[5]]$varU, M5.1$ETA[[6]]$varU,
M5.1$ETA[[7]]$varU, M5.1$ETA[[8]]$varU, M5.1$ETA[[9]]$varU,
M5.1$ETA[[10]]$varU, M5.1$varE)))
)
)
### CV 1 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 5, nrept = 10, cv = 'cv1', niter = 12000,
burnin = 2000)
M5.1_CV1 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 2 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = 2, nrept = 10, cv = 'cv2', niter = 12000,
burnin = 2000)
M5.1_CV2 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 0 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv0',
niter = 12000, burnin = 2000)
M5.1_CV0 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
### CV 00 --------------------------------------------------------------------
cvdata = assignation(data = pheno, y = 'EBLUE', gen = 'gen', env = 'env',
seed = 1000, nfolds = NA, nrept = NA, cv = 'loo_cv00',
niter = 12000, burnin = 2000)
M5.1_CV00 = crossvalid(cvdata = cvdata, ETA = ETA, results = NULL)
## Model 5.2 - GCA, SCA and Interactions w/ EnvCov and without Envcov - 4 stages ----
ETA = list(
E = list(V = eigen(ZZ_E)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
A = list(V = eigen(ZGZ_A)$vectors, d = eigen(ZZ_E)$values, model = 'RKHS'),
R = list(V = eigen(ZGZ_R)$vectors, d = eigen(ZGZ_R)$values, model = 'RKHS'),
H = list(V = eigen(ZGZ_H)$vectors, d = eigen(ZGZ_H)$values, model = 'RKHS'),
AW = list(V = eigen(GEIO_A_stages)$vectors, d = eigen(GEIO_A_stages)$values,
model = 'RKHS'),
RW = list(V = eigen(GEIO_R_stages)$vectors, d = eigen(GEIO_R_stages)$values,
model = 'RKHS'),
HW = list(V = eigen(GEIO_H_stages)$vectors, d = eigen(GEIO_H_stages)$values,
model = 'RKHS'),
AE = list(V = eigen(GEI_A)$vectors, d = eigen(GEI_A)$values, model = 'RKHS'),
RE = list(V = eigen(GEI_R)$vectors, d = eigen(GEI_R)$values, model = 'RKHS'),
HE = list(V = eigen(GEI_H)$vectors, d = eigen(GEI_R)$values, model = 'RKHS')
)
M5.2 = BGLR(y = pheno$EBLUE, ETA = ETA, nIter = 12000, burnIn = 2000)
unlink("*.dat")
pred_m5.2 = data.frame(
A_line = pheno$Aline,
R_line = pheno$Rline,
gen = pheno$gen,
env = pheno$env,
EBLUE = pheno$EBLUE,
GEBV = M5.2$yHat
)
varcomp_m5.2 = data.frame(
effect = c("E", 'Aline', 'Rline', 'Hybrid', 'AxW', 'RxW', 'HxW',
'AxE', 'RxE', 'HxE','e'),
varcomp = c(
M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU, M5.2$ETA[[4]]$varU,
M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU, M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU,
M5.2$ETA[[9]]$varU, M5.2$ETA[[10]]$varU, M5.2$varE
),
perc = c(
M5.2$ETA[[1]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[2]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[3]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[4]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[5]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[6]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[7]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,
M5.2$ETA[[10]]$varU, M5.2$varE)),
M5.2$ETA[[8]]$varU/sum(c(M5.2$ETA[[1]]$varU, M5.2$ETA[[2]]$varU, M5.2$ETA[[3]]$varU,
M5.2$ETA[[4]]$varU, M5.2$ETA[[5]]$varU, M5.2$ETA[[6]]$varU,
M5.2$ETA[[7]]$varU, M5.2$ETA[[8]]$varU, M5.2$ETA[[9]]$varU,