R/sim_yielddata.R
In jbkey730/BreedStats: Genetic values and predictions for breeding
Defines functions
sim_yielddata
G2P
G2P=function(X,h2,NQTN,distribution, me,s,sz,p,lm,name){
n=nrow(X)
m=ncol(X)
#Sampling QTN
QTN.position=sample(NQTN,NQTN*.01,replace=F)
SNPQ=as.matrix(X)
QTN.position
#QTN effects
if(distribution=="rnorm"){
addeffect= abs(rnorm(n=NQTN, mean=me, sd=s))
effectvar=var(addeffect)
residualvar=(effectvar-h2*effectvar)/h2
residual=rnorm(NQTN,0,sqrt(residualvar))
y=addeffect+residual
}
if(distribution=="rbinom"){
addeffect = abs(rbinom(n=NQTN, size=sz, prob=p))
effectvar=var(addeffect)
residualvar=(effectvar-h2*effectvar)/h2
residual=abs(rnorm(NQTN,0,sqrt(residualvar)))
y=addeffect+residual
y=round(y,0)
}
if(distribution=="rpois"){
addeffect = abs(rpois(n=NQTN,lambda=lm))
effectvar=var(addeffect)
residualvar=(effectvar-h2*effectvar)/h2
residual=abs(rnorm(NQTN,0,sqrt(residualvar)))
y=addeffect+residual
y=round(y,0)
}
#Simulate phenotype
rm(X,residual,residualvar,addeffect,effectvar,QTN.position,SNPQ,n,m,NQTN)
gc()
return(list( y=y))
}
sim_yielddata = function(BV.MC.Entry=BV.MC.Entry,seed=seed ){
seed=seed
#set.seed(seed)
#BV.MC.Entry.Choice=BV.MC.Entry %>% filter( `Book Season` == "21S: Corn")
# BV.MC.Entry.index = sample(nrow(BV.MC.Entry), nrow(BV.MC.Entry) * 0.5)
# BV.MC.Entry.sample = BV.MC.Entry[BV.MC.Entry.index,]
BV.MC.Entry.sample=data.frame(BV.MC.Entry)
names<-names(BV.MC.Entry.sample); names
BV.MC.Entry.sampleEBN.index = BV.MC.Entry.sample[!duplicated(BV.MC.Entry.sample$Pedigree),"Pedigree"]
traitList = c("Yield", "Plt Height", "Y.M", "Silk.Color",
"EarHt", "EarHt..Rating.", "StandCnt..UAV.", "Tassel.Branch..", "NCLB",
"GS.Late", "Plt.Height..Rating.", "SL.Rating..Early.", "Tassel.Extension", "NCLB.2",
"PCT.HOH", "Glume.Color", "TillerCnt",
"Plt.Height", "Glume.Ring", "Ears.Per.Stalk",
"RL..", "Leaf.Angle..Inb.Obs.", "Anther.Color",
"RL.Count", "Leaf.Color", "Pollen.Duration..GDUs.10..to.90..", "Brace.Root.Color",
"Test.WT", "Leaf.Texture", "SL.Rating",
"SL..", "RL.Rating","SL.Count", "Pollen.Shed.Rating",
"Cob.Color", "StandCnt..Final.", "GOSS","GLS",
"Silk.GDU.s.to.10." ,"Silk.GDU.s.to.50.","Silk.GDU.s.to.90.",
"Pollen.GDU.s.to.10.", "Pollen.GDU.s.to.50.", "Pollen.GDU.s.to.90.")
for(name in names){
# bind.linked.male.peds=foreach(i=(1:length(BV.MC.Entry.sampleEBN.index)),
# .combine = 'cbind',
# .inorder=F
#
# #.packages=c(),
# # .export=c()
# ) %dopar% {
if( name %in% (traitList) ){
cat("Simulating ", name,"... \n")
for(i in 1:length(BV.MC.Entry.sampleEBN.index)){
EBNt = BV.MC.Entry.sampleEBN.index[[i]]
subsetSA = subset(
BV.MC.Entry.sample[,c("Pedigree",paste0(name),"Entry.Book.Name")],
Pedigree==EBNt)
subsetSA$Pedigree = as.character(subsetSA$Pedigree)
BV.MC.Entry.sample$Pedigree = as.character(BV.MC.Entry.sample$Pedigree)
if(name == "Yield"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.23, distribution = "rnorm", me=100:300, s=20)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "EarHt"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=20:70, s=.28)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "GS.Late"){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=1:60, p=.02)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "PCT.HOH"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.27, distribution = "rnorm", me=15:30, s=.5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "Plt.Height"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.23, distribution = "rnorm", me=80:120, s=15)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "RL.."){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.11, distribution = "rbinom", sz=1:50, p=.04)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "RL.Count"){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.10, distribution = "rbinom", sz=1:70, p=.05)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "Test.WT"){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.40, distribution = "rnorm", me=55:62, s=10)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "SL.."){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.44, distribution = "rbinom", sz=1:50, p=.11)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "SL.Count"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.34, distribution = "rbinom", sz=1:50, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "Y.M"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rpois",lm=9:15)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "StandCnt..UAV."){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.37, distribution = "rnorm", me=60:70, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name=="SL.Rating..Early."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.2, distribution = "rnorm", me=50, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.GDU.s.to.10."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1250, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.GDU.s.to.50."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1350, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.GDU.s.to.90."){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1450, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.GDU.s.to.10."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1250, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.GDU.s.to.50."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1350, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.GDU.s.to.90."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1450, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Cob.Color"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "EarHt"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=45, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Plt.Height"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.4, distribution = "rnorm", me=75, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "EarHt..Rating."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=35, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Plt.Height..Rating."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=75, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Glume.Color"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Glume.Ring"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Leaf.Angle..Inb.Obs."){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=25, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Leaf.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Leaf.Texture"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.Duration..GDUs.10..to.90.."){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=65, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.Shed.Rating"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Tassel.Branch.."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=5, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Tassel.Extension"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=5, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "TillerCnt"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=0, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Ears.Per.Stalk"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=1, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Anther.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Brace.Root.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "SL.Rating"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=50, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "RL.Rating"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=50, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "StandCnt..Final."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=75, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "NCLB"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "NCLB.2"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "GOSS"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "GLS"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=10, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
rm(ID)
invisible(gc())
#hist(BV.MC.Entry.sample$Y.M)
}
}
}
#stopCluster(cl)
cat("Done simulating!")
return(data.frame(BV.MC.Entry.sample))
}
# BV.MC.Entry.Choice=BV.MC.Entry %>% filter(grepl(x=`Entry Book Name`, pattern="TP"))
#
#BV.MC.Entry = sim_yielddata(BV.MC.Entry = BV.MC.Entry, seed=8042)
#
# # BV.MC.NUR = sim_yielddata(BV.MC.Entry = BV.MC.NUR, seed=8042)
# # BV.MC.GOSS = sim_yielddata(BV.MC.Entry = BV.MC.GOSS, seed=8042)
# # #
# # save(BV.MC.GOSS,file="inbredgossdata.rda")
# # save(BV.MC.NUR,file="inbreddata.rda")
#
# save(BV.MC.Entry,file="bvdata.rda")
jbkey730/BreedStats documentation built on Aug. 28, 2022, 8:22 a.m.
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Defines functions sim_yielddata G2P
G2P=function(X,h2,NQTN,distribution, me,s,sz,p,lm,name){
n=nrow(X)
m=ncol(X)
#Sampling QTN
QTN.position=sample(NQTN,NQTN*.01,replace=F)
SNPQ=as.matrix(X)
QTN.position
#QTN effects
if(distribution=="rnorm"){
addeffect= abs(rnorm(n=NQTN, mean=me, sd=s))
effectvar=var(addeffect)
residualvar=(effectvar-h2*effectvar)/h2
residual=rnorm(NQTN,0,sqrt(residualvar))
y=addeffect+residual
}
if(distribution=="rbinom"){
addeffect = abs(rbinom(n=NQTN, size=sz, prob=p))
effectvar=var(addeffect)
residualvar=(effectvar-h2*effectvar)/h2
residual=abs(rnorm(NQTN,0,sqrt(residualvar)))
y=addeffect+residual
y=round(y,0)
}
if(distribution=="rpois"){
addeffect = abs(rpois(n=NQTN,lambda=lm))
effectvar=var(addeffect)
residualvar=(effectvar-h2*effectvar)/h2
residual=abs(rnorm(NQTN,0,sqrt(residualvar)))
y=addeffect+residual
y=round(y,0)
}
#Simulate phenotype
rm(X,residual,residualvar,addeffect,effectvar,QTN.position,SNPQ,n,m,NQTN)
gc()
return(list( y=y))
}
sim_yielddata = function(BV.MC.Entry=BV.MC.Entry,seed=seed ){
seed=seed
#set.seed(seed)
#BV.MC.Entry.Choice=BV.MC.Entry %>% filter( `Book Season` == "21S: Corn")
# BV.MC.Entry.index = sample(nrow(BV.MC.Entry), nrow(BV.MC.Entry) * 0.5)
# BV.MC.Entry.sample = BV.MC.Entry[BV.MC.Entry.index,]
BV.MC.Entry.sample=data.frame(BV.MC.Entry)
names<-names(BV.MC.Entry.sample); names
BV.MC.Entry.sampleEBN.index = BV.MC.Entry.sample[!duplicated(BV.MC.Entry.sample$Pedigree),"Pedigree"]
traitList = c("Yield", "Plt Height", "Y.M", "Silk.Color",
"EarHt", "EarHt..Rating.", "StandCnt..UAV.", "Tassel.Branch..", "NCLB",
"GS.Late", "Plt.Height..Rating.", "SL.Rating..Early.", "Tassel.Extension", "NCLB.2",
"PCT.HOH", "Glume.Color", "TillerCnt",
"Plt.Height", "Glume.Ring", "Ears.Per.Stalk",
"RL..", "Leaf.Angle..Inb.Obs.", "Anther.Color",
"RL.Count", "Leaf.Color", "Pollen.Duration..GDUs.10..to.90..", "Brace.Root.Color",
"Test.WT", "Leaf.Texture", "SL.Rating",
"SL..", "RL.Rating","SL.Count", "Pollen.Shed.Rating",
"Cob.Color", "StandCnt..Final.", "GOSS","GLS",
"Silk.GDU.s.to.10." ,"Silk.GDU.s.to.50.","Silk.GDU.s.to.90.",
"Pollen.GDU.s.to.10.", "Pollen.GDU.s.to.50.", "Pollen.GDU.s.to.90.")
for(name in names){
# bind.linked.male.peds=foreach(i=(1:length(BV.MC.Entry.sampleEBN.index)),
# .combine = 'cbind',
# .inorder=F
#
# #.packages=c(),
# # .export=c()
# ) %dopar% {
if( name %in% (traitList) ){
cat("Simulating ", name,"... \n")
for(i in 1:length(BV.MC.Entry.sampleEBN.index)){
EBNt = BV.MC.Entry.sampleEBN.index[[i]]
subsetSA = subset(
BV.MC.Entry.sample[,c("Pedigree",paste0(name),"Entry.Book.Name")],
Pedigree==EBNt)
subsetSA$Pedigree = as.character(subsetSA$Pedigree)
BV.MC.Entry.sample$Pedigree = as.character(BV.MC.Entry.sample$Pedigree)
if(name == "Yield"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.23, distribution = "rnorm", me=100:300, s=20)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "EarHt"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=20:70, s=.28)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "GS.Late"){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=1:60, p=.02)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "PCT.HOH"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.27, distribution = "rnorm", me=15:30, s=.5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "Plt.Height"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.23, distribution = "rnorm", me=80:120, s=15)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "RL.."){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.11, distribution = "rbinom", sz=1:50, p=.04)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "RL.Count"){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.10, distribution = "rbinom", sz=1:70, p=.05)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "Test.WT"){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.40, distribution = "rnorm", me=55:62, s=10)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "SL.."){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.44, distribution = "rbinom", sz=1:50, p=.11)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "SL.Count"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.34, distribution = "rbinom", sz=1:50, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "Y.M"){
subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rpois",lm=9:15)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name == "StandCnt..UAV."){
subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.37, distribution = "rnorm", me=60:70, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name=="SL.Rating..Early."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.2, distribution = "rnorm", me=50, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.GDU.s.to.10."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1250, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.GDU.s.to.50."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1350, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.GDU.s.to.90."){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1450, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.GDU.s.to.10."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1250, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.GDU.s.to.50."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1350, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.GDU.s.to.90."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=1450, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Cob.Color"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "EarHt"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=45, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Plt.Height"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.4, distribution = "rnorm", me=75, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "EarHt..Rating."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=35, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Plt.Height..Rating."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=75, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Glume.Color"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Glume.Ring"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Leaf.Angle..Inb.Obs."){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=25, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Leaf.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Leaf.Texture"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.Duration..GDUs.10..to.90.."){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.61, distribution = "rnorm", me=65, s=5)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Pollen.Shed.Rating"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Silk.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Tassel.Branch.."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=5, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Tassel.Extension"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=5, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "TillerCnt"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=0, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Ears.Per.Stalk"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=1, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Anther.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "Brace.Root.Color"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "SL.Rating"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=50, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "RL.Rating"){subsetSA[,name] =G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=50, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "StandCnt..Final."){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=75, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "NCLB"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "NCLB.2"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "GOSS"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=3, p=.9)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
if(name== "GLS"){subsetSA[,name] = G2P(X=subsetSA,NQTN=nrow(subsetSA), h2=.48, distribution = "rbinom", sz=10, p=.1)$y
ID = which(BV.MC.Entry.sample$Pedigree == EBNt)
BV.MC.Entry.sample[ID, name] <- subsetSA[ name]
}
rm(ID)
invisible(gc())
#hist(BV.MC.Entry.sample$Y.M)
}
}
}
#stopCluster(cl)
cat("Done simulating!")
return(data.frame(BV.MC.Entry.sample))
}
# BV.MC.Entry.Choice=BV.MC.Entry %>% filter(grepl(x=`Entry Book Name`, pattern="TP"))
#
#BV.MC.Entry = sim_yielddata(BV.MC.Entry = BV.MC.Entry, seed=8042)
#
# # BV.MC.NUR = sim_yielddata(BV.MC.Entry = BV.MC.NUR, seed=8042)
# # BV.MC.GOSS = sim_yielddata(BV.MC.Entry = BV.MC.GOSS, seed=8042)
# # #
# # save(BV.MC.GOSS,file="inbredgossdata.rda")
# # save(BV.MC.NUR,file="inbreddata.rda")
#
# save(BV.MC.Entry,file="bvdata.rda")
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