R/anteBayesBm.R
In chenchunyu88/BATools: BATools: An R package that extends 'Bayesian Alphabet' for Whole Genome Prediction and Genome-wide association study
###################################################################################################
############### anteBayesB ########################################
###################################################################################################
anteBayesBm = function(op=NULL,y=NULL,Z=NULL,X=NULL,vtrain=NULL,GWA=NULL,map=NULL,ante_p=NULL)
# startpi is defined by the proportion of markers that ARE associate with genes
{ # START OF FUNCTION
set.seed(op$seed)
pi_math = 3.14159265359
whichNa=which(vtrain==FALSE)
Z0=Z
y0=y
X0=X
if(length(whichNa)>0)
{
y=y[-whichNa]
Z=Z[-whichNa,]
X=X[-whichNa,]
}
if(GWA=="Win") {
win=c()
for(j in 1:max(map$idw)){
win=append(win,sum(map$idw==j))
}
chr=map %>% distinct(chr,idw) %>% select(chr) %>% t %>% as.numeric
}else{
win=NULL
chr=NULL
}
# Vectors for saved samples of hyperparameters
if (op$update_para$df) {defsave = array(0,op$run_para$niter/op$run_para$skip) }
if (op$update_para$scale) {scalesave = array(0,op$run_para$niter/op$run_para$skip) }
if (op$update_para$pi) {pisave = array(0,op$run_para$niter/op$run_para$skip) }
varesave = array(0,op$run_para$niter/op$run_para$skip)
if (op$update_para$mut) {mutsave = array(0,op$run_para$niter/op$run_para$skip)}
if (op$update_para$vart) { vartsave = array(0,op$run_para$niter/op$run_para$skip) }
# To monitor MH acceptance ratios on degrees of freedom parameter
alphadef_save = array(0,((op$run_para$niter-op$run_para$burnIn)))
# TO MONITOR MH ACCEPTANCE RATIOS ON SCALE PARAMETER (WHERE NECESSARY) POST BURN-IN
alphascalea_save = array(0,(op$run_para$niter-op$run_para$burnIn)/op$run_para$skip)
nx=rownames(X)
ng=rownames(Z)
np=names(y)
idx <- Reduce(intersect, list(nx,ng,np))
if(dim(X)[2]==1) X=as.matrix(X[idx,],ncol=1) else X=X[idx,]
y=y[idx]
Z=Z[idx,]
dimX=dim(X)[2]
nSNP=dim(Z)[2] #number of SNP
nanim=dim(Z)[1]; #number of animals
nrecords = length(y)
G1=nSNP
W = as.matrix(cbind(X,Z))
Wstacked=as.vector(W)
M = W[,-(1:dimX)]
Mstacked=as.vector(M)
# specify prior degrees of belief on genetic variance
# specify prior scale on genetic variance
# inital values
def=op$init$df # Starting Bayes A df
scalea = op$init$scale # Starting scale parameter
Pi_SNP = op$init$pi # Starting pi parameter (probability of being a gene!)
mut = op$init$mut
vart =op$init$vart
cdef = op$priors$cdef # cdef is scale parameter for proposal density on df
cscalea = op$priors$cscalea
alphacheck = 0
alphaQTLvar= 0 # to monitor BayesB QTLvar rejection rates
alphatot1= 0
if (op$update_para$scale & op$update_para$df) alphatot2= 0
WWdiag = apply(W,2,crossprod) # diagonals are important
# used to monitor Metropolis Hastings acceptance ratio for df
dimW = dim(W)[2] # number of columns = # of location parameters
# initialize a few arrays
theta = array(0,dimW)
SNPeff = array(0,nSNP)
assoct = array(0,nSNP)
SNPg = array(0,nSNP)
varq = array(scalea,nSNP) # initialize each SNP variance to overall scale parameter
SNPvar = array(0,nSNP) # used to take running sum of SNP specific variances
SNPvarlast = array(0,nSNP)
postprob = array(0,nSNP) # indicator variable for inclusion of SNP in model
# later used to determine posterior probability...unique to BayesB
postprob_save=array(0,nSNP)
if(GWA=="Win")
{
p1=array(0,nSNP)
p2=array(0,nSNP)
pw=array(0,nSNP)
}
alphametrovar = array(0,nSNP) # used to monitor MH acceptance rates for included genes.
# This is for computing mean and variance of each SNP effect.
Mlast = array(0,dimW)
Qlast = array(0,dimW)
Mcurrent = array(0,dimW)
Qcurrent = array(0,dimW)
Tlast= array(0,nSNP)
Tcurrent= array(0,nSNP)
TQlast= array(0,nSNP)
TQcurrent= array(0,nSNP)
# adjust y
ycorr = y - as.vector(W%*%theta) # adjust obs by everything before starting iterations
# so this is the residual.
# use this to set up the starting value for the residual variance
if(is.null(op$init$vare)) vare = crossprod(ycorr)/(nrecords-dimX) else vare=op$init$vare
starttime<-proc.time()
timeSNP = 0
timetotal=0
# mcmc sampling
for (iter in 1:op$run_para$niter)
{ #"START" OF MCMC SAMPLING
itersave = iter-op$run_para$burnIn
if (itersave ==1)
{
alphametrovar = array(0,nSNP) # reinitialize to 0 post burn-in
if(op$model=="anteBayesB") postprob = array(0,nSNP) # reinitialize to 0 post burn-in
}
#####################################################################################
# sample intercept
#####################################################################################
for(i in 1:dimX)
{
ycorr = ycorr + W[,i]*theta[i]
rhs=t(W[,i])%*%ycorr/vare# 1'(y-W*theta)/sigmae
lhs=WWdiag[i]/vare
invLhs=1.0/lhs
meancond = rhs*invLhs
theta[i] = rnorm(1,meancond,sqrt(invLhs))
ycorr = ycorr - W[,i]*theta[i] # take it off again to create new "residual"
}
#####################################################################################
# sample variance & effect for each SNP
####################################################################################
if (Pi_SNP == 1)
{ # "START" OF anteBAYESA
#####################################################################################
# sample conditional variance for each SNP (anteBayes A)
#####################################################################################
varq = (scalea*def + SNPeff*SNPeff)/rchisq(nSNP,def+1) # joint sample
#####################################################################################
# sample conditional effect for each SNP (anteBayes A)
#####################################################################################
startSNP = proc.time()
BayesC<- .Call("anteBayesAC",assoct,nSNP,dimX,nanim,Wstacked,WWdiag, theta, ycorr,varq,vare,SNPeff,SNPg,mut,vart)
theta=BayesC[[1]]
ycorr=BayesC[[2]]
varq=BayesC[[3]]
vare=BayesC[[4]]
SNPeff=BayesC[[5]]
SNPg=BayesC[[6]]
assoct=BayesC[[7]]
if(!is.null(ante_p)) assoct[ante_p]=0
G1=nSNP
} # "END" FOR anteBAYESA
#####################################################################################
# sample effect for each SNP (anteBayes B)
#####################################################################################
if (Pi_SNP < 1 )
{ # "START" OF anteBAYESB
startSNP = proc.time()
G1=rep(0,2)
#####################################################################################
anteBayesB<-.Call("anteBayesBC",assoct,nSNP,dimX,nanim,Wstacked,Mstacked,WWdiag, theta,ycorr,varq,vare,SNPeff,SNPg,postprob,scalea,def,alphametrovar,Pi_SNP,G1,mut,vart)
theta=anteBayesB[[1]]
ycorr=anteBayesB[[2]]
varq=anteBayesB[[3]]
vare=anteBayesB[[4]]
SNPeff=anteBayesB[[5]]
###This is trick pointer...
p1=anteBayesB[[6]]
postprob=anteBayesB[[6]]
postprob_save=postprob_save+postprob
postprob = array(0,nSNP)
alphametrovar=anteBayesB[[7]]
G1=anteBayesB[[8]][1] #add G1
def=anteBayesB[[9]]
scalea=anteBayesB[[10]]
Pi_SNP=anteBayesB[[11]]
alphacheck=anteBayesB[[12]][1]
SNPg=anteBayesB[[13]]
assoct=anteBayesB[[14]]
if(!is.null(ante_p)) assoct[ante_p]=0
} # "END" OF BAYESB
#####################################################################################
# sample residual variance
#####################################################################################
if(op$update_para$vare) vare = ( crossprod(ycorr) +op$priors$tau2_e )/rchisq(1,nrecords+op$priors$nu_e)
# scale parameter should be e`e + Se,
# degrees of freedom should be n + ve
#####################################################################################
# sample degrees of freedom
#####################################################################################
if (op$update_para$df)
{ #"START" OF SAMPLE THE DEGREES OF FREEDOM
SNPeff2=SNPeff*SNPeff
for (cycle in 1:10){
mhdf=metropDf(def, scalea, G1=nSNP, SNPeff2, priordf = 1, cdef , max = Inf,pi_math=pi_math)
def=as.numeric(mhdf[[1]])
alpha1=as.numeric(mhdf[[2]])
alphatot1=alphatot1+alpha1
if (cycle==10 & iter <= op$run_para$burnIn)
{
alphacheck1 = alphatot1/10;
if (alphacheck1 > .70) { cdef = cdef*1.2}
else if(alphacheck1 < .20) {cdef = cdef*.7} # tune Metropolis sampler #
alphatot1 = 0;
}
else if (cycle==10 & iter > op$run_para$burnIn)
{
alphacheck1 = alphatot1/10;
alphadef_save[itersave/op$run_para$skip] = alphacheck1
alphatot1 = 0
}
}
} # "END" OF SAMPLE THE DEGREES OF FREEDOM
#####################################################################################
# sample scale#
# based on Gelman Uniform(0,A) prior on scale parameter for "large" A.
#####################################################################################
if (op$update_para$scale && !(op$update_para$df))
{ #"START" OF SAMPLE THE SCALE PARAMETER
shape_g = 0.5*(G1*def+1)+op$priors$shape_scale
scale_g = 0.5*def*sum(1/varq[which(varq>0)])+op$priors$rate_scale
scalea=rgamma(1,shape=shape_g,rate=scale_g)
} #"END" OF SAMPLE THE SCALE PARAMETER
#####################################################################################
# sample scale usning UNIMH#
#####################################################################################
if (op$update_para$scale & op$update_para$df)
{ #"START" OF SAMPLE THE SCALE PARAMETER
SNPeff2=SNPeff*SNPeff
for (cycle in 1:10){
mhscale=metropScale(def, scalea, G1=G1, SNPeff2, cscalea,op$priors$shape_scale, op$priors$rate_scale, pi_math=pi_math)
scalea=as.numeric(mhscale[[1]])
alpha2=as.numeric(mhscale[[2]])
alphatot2=alphatot2+alpha2
if (cycle==10 & iter <= op$run_para$burnIn)
{
alphacheck2 = alphatot2/10;
if (alphacheck2 > .70) { cscalea = cscalea*1.2}
else if(alphacheck2 < .20) {cscalea = cscalea*.7} # tune Metropolis sampler #
alphatot2 = 0;
}
else if (cycle==10 & iter > op$run_para$burnIn)
{
alphacheck2 = alphatot2/10;
alphascalea_save[itersave/op$run_para$skip] = alphacheck2
alphatot2 = 0
}
}
} #"END" OF SAMPLE THE SCALE PARAMETER
#####################################################################################
# sample pi
#####################################################################################
if (op$update_para$pi)
{
Pi_SNP = rbeta(1,op$priors$alphapi+G1,op$priors$betapi+nSNP-G1)
} #"END" OF SAMPLE THE PI PARAMETER
#####################################################################################
# sample mean and variance for association parameters
#####################################################################################
if (op$update_para$mut)
{
######sample mut#######
sigma2hat_t=1/(1/op$priors$sigma2_m_t+length(which(assoct!=0))/vart)
muhat_t=((mean(assoct[which(assoct!=0)])*length(which(assoct!=0))/vart)+(op$priors$mu_m_t/op$priors$sigma2_m_t))*sigma2hat_t
mut = rnorm(1,mean=muhat_t,sd=sqrt(sigma2hat_t))
}
########sample vart#######################
if(op$update_para$vart)
{
vart = ( t(assoct[which(assoct!=0)]-mut)%*%(assoct[which(assoct!=0)]-mut)+1)/rchisq(1,(length(assoct[which(assoct!=0)])-1)+3)
}
if (itersave>1)
{ # "START" OF COMPUTE POSTERIOR MEANS & VARIANCES
Mcurrent = Mlast + (theta-Mlast)/itersave
Qcurrent = Qlast + (itersave-1)*((theta-Mlast)^2)/itersave
Mlast = Mcurrent
Qlast = Qcurrent
SNPvar=SNPvarlast + (varq-SNPvarlast)/itersave
SNPvarlast=SNPvar
Tcurrent=Tlast+ (assoct-Tlast)/itersave
Tlast=Tcurrent
TQcurrent= TQlast+(itersave-1)*((assoct-Tlast)^2)/itersave
TQlast=TQcurrent
if(GWA=="Win"){
Wpostprob=Wpostprob+calc.phiw(win,p1)
n_saved_sample=n_saved_sample+1
}
} # "END" OF COMPUTE POSTERIOR MEANS & VARIANCES
if (itersave==1) {
Mlast = theta;Tlast= assoct;SNPvarlast=varq;TQlast=rep(vart,length(assoct))
Wpostprob=rep(0,length(win))
n_saved_sample=0
}
###########################################################################################
#store MCMC samples at every "skip"
###########################################################################################
if (iter%%op$run_para$skip == 0)
{
varesave[iter/op$run_para$skip] = vare
if(op$update_para$df) {defsave[iter/op$run_para$skip] = def}
if(op$update_para$scale) {scalesave[iter/op$run_para$skip] = scalea}
if(op$update_para$pi) {pisave[iter/op$run_para$skip] = Pi_SNP}
if(op$update_para$mut){mutsave[iter/op$run_para$skip]=mut}
if(op$update_para$vart){vartsave[iter/op$run_para$skip]=vart}
}
endSNP = proc.time()
timeSNP = timeSNP + (endSNP[1]-startSNP[1])
endtime = proc.time()
timetotal = endtime[1]-starttime[1]
if((op$print_mcmc$piter!=0) & iter%%op$print_mcmc$piter==0)
{
tmp=paste("iter= ",iter," vare= ",round(vare,6), "scale= ", round(scalea,6),
"pi= ",round(Pi_SNP,6),
"\ntimepercycle= ", round(timeSNP/iter,3), "estimated time left=", round(timeSNP/iter*(op$run_para$niter-iter),2),"\n",sep=" ")
if(op$print_mcmc$print_to=="screen")
{
cat(tmp)
}else{
write(tmp, file = paste("log",op$seed,".txt",sep=""),append = TRUE)
}
}
if (iter%%1000==0)
{
#############################################################################################
# PROCESSING MCMC SAMPLES
#############################################################################################
hyperparameters=c()
counth=0
hyperparameters = cbind(hyperparameters,varesave);counth=counth+1
hyperparameters = cbind(hyperparameters,scalesave) ;counth=counth+1
#if (op$update_para$scale) {hyperparameters = cbind(hyperparameters,scalesave);counth=counth+1 }
if (op$update_para$df) {hyperparameters = cbind(hyperparameters,defsave);counth=counth+1 }
if (op$update_para$pi) {hyperparameters = cbind(hyperparameters,pisave);counth=counth+1 }
if (op$update_para$mut) {hyperparameters = cbind(hyperparameters,mutsave);counth=counth+1}
if (op$update_para$vart) {hyperparameters = cbind(hyperparameters,vartsave) ;counth=counth+1}
if(counth)rownames(hyperparameters) = seq(op$run_para$skip,op$run_para$niter,op$run_para$skip)
save(iter,hyperparameters,file=paste(op$save.at, "Hyperparameters",op$seed,".RData",sep=""))
SNPtimepercycle = timeSNP/iter
timepercycle=timetotal/iter
save(iter,timepercycle,SNPtimepercycle,file=paste(op$save.at, "time",op$seed,".RData",sep=""))
}# "END" OF PROCESSING MCMC SAMPLING
if (iter%%1000==0 & itersave>1 )
{
#############################################################################################
# PROCESSING MCMC SAMPLES
#############################################################################################
postprob_est = postprob_save/itersave
if(GWA=="Win") Wpostprob_est =Wpostprob/n_saved_sample else Wpostprob_est=NULL
names(postprob_est)=colnames(Z)
meanmu= Mcurrent[1:dimX]
names(meanmu)=colnames(X)
meang= Mcurrent[(dimX+1):dimW]
varg = Qcurrent[(dimX+1):dimW]/itersave
names(meang)=names(varg)=colnames(Z)
meant= Tcurrent[1:(nSNP-1)]
vart=TQcurrent[1:(nSNP-1)]/itersave
names(meant)=names(vart)=names(meang)[-1]
save(iter,meanmu,meang,varg,SNPvar,meant,vart,postprob_est,Wpostprob_est,file = paste(op$save.at,"EffectsResults",op$seed,".RData",sep=""))
# monitor MH acceptance rates over cycle for the degrees of freedom term.
save(iter,alphadef_save,alphascalea_save,file=paste(op$save.at, "alphadef",op$seed,".RData",sep=""))
} # "END" OF PROCESSING MCMC SAMPLING
} # "END" OF MCMC SAMPLING
names_hypers=c()
names_hypers=c(names_hypers,"vare")
names_hypers = c(names_hypers,"scale")
if (op$update_para$df) {names_hypers = c(names_hypers,"df") }
if (op$update_para$pi) {names_hypers = c(names_hypers,"pi") }
if (op$update_para$mut) names_hypers = c(names_hypers,"mut")
if (op$update_para$vart) names_hypers = c(names_hypers,"vart")
sample_idx=c((op$run_para$burnIn/op$run_para$skip+1):(op$run_para$niter/op$run_para$skip))
if(length(names_hypers)>1) hyper_est=apply(hyperparameters[sample_idx,],2,mean)
else if (length(names_hypers)==1) hyper_est=mean(hyperparameters[sample_idx,])
else hyper_est=c(vare,scale)
colnames(hyperparameters)=names_hypers
if(length(names_hypers)>1) names(hyper_est)=names_hypers
BAout<-list(bhat=meanmu,ahat=meang, yhat=X0%*%meanmu+Z0%*%meang,
prob=postprob_est,Wprob=Wpostprob_est,ante_t=meant,eff_sample=effectiveSize(hyperparameters),
hypers=hyperparameters,idx=idx,hyper_est=hyper_est,train=vtrain,y=y0,map=map,win=win,
Wchr=chr,GWA=GWA,model=op$model)
class(BAout)="ba"
return(BAout)
} # END OF FUNCTION
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###################################################################################################
############### anteBayesB ########################################
###################################################################################################
anteBayesBm = function(op=NULL,y=NULL,Z=NULL,X=NULL,vtrain=NULL,GWA=NULL,map=NULL,ante_p=NULL)
# startpi is defined by the proportion of markers that ARE associate with genes
{ # START OF FUNCTION
set.seed(op$seed)
pi_math = 3.14159265359
whichNa=which(vtrain==FALSE)
Z0=Z
y0=y
X0=X
if(length(whichNa)>0)
{
y=y[-whichNa]
Z=Z[-whichNa,]
X=X[-whichNa,]
}
if(GWA=="Win") {
win=c()
for(j in 1:max(map$idw)){
win=append(win,sum(map$idw==j))
}
chr=map %>% distinct(chr,idw) %>% select(chr) %>% t %>% as.numeric
}else{
win=NULL
chr=NULL
}
# Vectors for saved samples of hyperparameters
if (op$update_para$df) {defsave = array(0,op$run_para$niter/op$run_para$skip) }
if (op$update_para$scale) {scalesave = array(0,op$run_para$niter/op$run_para$skip) }
if (op$update_para$pi) {pisave = array(0,op$run_para$niter/op$run_para$skip) }
varesave = array(0,op$run_para$niter/op$run_para$skip)
if (op$update_para$mut) {mutsave = array(0,op$run_para$niter/op$run_para$skip)}
if (op$update_para$vart) { vartsave = array(0,op$run_para$niter/op$run_para$skip) }
# To monitor MH acceptance ratios on degrees of freedom parameter
alphadef_save = array(0,((op$run_para$niter-op$run_para$burnIn)))
# TO MONITOR MH ACCEPTANCE RATIOS ON SCALE PARAMETER (WHERE NECESSARY) POST BURN-IN
alphascalea_save = array(0,(op$run_para$niter-op$run_para$burnIn)/op$run_para$skip)
nx=rownames(X)
ng=rownames(Z)
np=names(y)
idx <- Reduce(intersect, list(nx,ng,np))
if(dim(X)[2]==1) X=as.matrix(X[idx,],ncol=1) else X=X[idx,]
y=y[idx]
Z=Z[idx,]
dimX=dim(X)[2]
nSNP=dim(Z)[2] #number of SNP
nanim=dim(Z)[1]; #number of animals
nrecords = length(y)
G1=nSNP
W = as.matrix(cbind(X,Z))
Wstacked=as.vector(W)
M = W[,-(1:dimX)]
Mstacked=as.vector(M)
# specify prior degrees of belief on genetic variance
# specify prior scale on genetic variance
# inital values
def=op$init$df # Starting Bayes A df
scalea = op$init$scale # Starting scale parameter
Pi_SNP = op$init$pi # Starting pi parameter (probability of being a gene!)
mut = op$init$mut
vart =op$init$vart
cdef = op$priors$cdef # cdef is scale parameter for proposal density on df
cscalea = op$priors$cscalea
alphacheck = 0
alphaQTLvar= 0 # to monitor BayesB QTLvar rejection rates
alphatot1= 0
if (op$update_para$scale & op$update_para$df) alphatot2= 0
WWdiag = apply(W,2,crossprod) # diagonals are important
# used to monitor Metropolis Hastings acceptance ratio for df
dimW = dim(W)[2] # number of columns = # of location parameters
# initialize a few arrays
theta = array(0,dimW)
SNPeff = array(0,nSNP)
assoct = array(0,nSNP)
SNPg = array(0,nSNP)
varq = array(scalea,nSNP) # initialize each SNP variance to overall scale parameter
SNPvar = array(0,nSNP) # used to take running sum of SNP specific variances
SNPvarlast = array(0,nSNP)
postprob = array(0,nSNP) # indicator variable for inclusion of SNP in model
# later used to determine posterior probability...unique to BayesB
postprob_save=array(0,nSNP)
if(GWA=="Win")
{
p1=array(0,nSNP)
p2=array(0,nSNP)
pw=array(0,nSNP)
}
alphametrovar = array(0,nSNP) # used to monitor MH acceptance rates for included genes.
# This is for computing mean and variance of each SNP effect.
Mlast = array(0,dimW)
Qlast = array(0,dimW)
Mcurrent = array(0,dimW)
Qcurrent = array(0,dimW)
Tlast= array(0,nSNP)
Tcurrent= array(0,nSNP)
TQlast= array(0,nSNP)
TQcurrent= array(0,nSNP)
# adjust y
ycorr = y - as.vector(W%*%theta) # adjust obs by everything before starting iterations
# so this is the residual.
# use this to set up the starting value for the residual variance
if(is.null(op$init$vare)) vare = crossprod(ycorr)/(nrecords-dimX) else vare=op$init$vare
starttime<-proc.time()
timeSNP = 0
timetotal=0
# mcmc sampling
for (iter in 1:op$run_para$niter)
{ #"START" OF MCMC SAMPLING
itersave = iter-op$run_para$burnIn
if (itersave ==1)
{
alphametrovar = array(0,nSNP) # reinitialize to 0 post burn-in
if(op$model=="anteBayesB") postprob = array(0,nSNP) # reinitialize to 0 post burn-in
}
#####################################################################################
# sample intercept
#####################################################################################
for(i in 1:dimX)
{
ycorr = ycorr + W[,i]*theta[i]
rhs=t(W[,i])%*%ycorr/vare# 1'(y-W*theta)/sigmae
lhs=WWdiag[i]/vare
invLhs=1.0/lhs
meancond = rhs*invLhs
theta[i] = rnorm(1,meancond,sqrt(invLhs))
ycorr = ycorr - W[,i]*theta[i] # take it off again to create new "residual"
}
#####################################################################################
# sample variance & effect for each SNP
####################################################################################
if (Pi_SNP == 1)
{ # "START" OF anteBAYESA
#####################################################################################
# sample conditional variance for each SNP (anteBayes A)
#####################################################################################
varq = (scalea*def + SNPeff*SNPeff)/rchisq(nSNP,def+1) # joint sample
#####################################################################################
# sample conditional effect for each SNP (anteBayes A)
#####################################################################################
startSNP = proc.time()
BayesC<- .Call("anteBayesAC",assoct,nSNP,dimX,nanim,Wstacked,WWdiag, theta, ycorr,varq,vare,SNPeff,SNPg,mut,vart)
theta=BayesC[[1]]
ycorr=BayesC[[2]]
varq=BayesC[[3]]
vare=BayesC[[4]]
SNPeff=BayesC[[5]]
SNPg=BayesC[[6]]
assoct=BayesC[[7]]
if(!is.null(ante_p)) assoct[ante_p]=0
G1=nSNP
} # "END" FOR anteBAYESA
#####################################################################################
# sample effect for each SNP (anteBayes B)
#####################################################################################
if (Pi_SNP < 1 )
{ # "START" OF anteBAYESB
startSNP = proc.time()
G1=rep(0,2)
#####################################################################################
anteBayesB<-.Call("anteBayesBC",assoct,nSNP,dimX,nanim,Wstacked,Mstacked,WWdiag, theta,ycorr,varq,vare,SNPeff,SNPg,postprob,scalea,def,alphametrovar,Pi_SNP,G1,mut,vart)
theta=anteBayesB[[1]]
ycorr=anteBayesB[[2]]
varq=anteBayesB[[3]]
vare=anteBayesB[[4]]
SNPeff=anteBayesB[[5]]
###This is trick pointer...
p1=anteBayesB[[6]]
postprob=anteBayesB[[6]]
postprob_save=postprob_save+postprob
postprob = array(0,nSNP)
alphametrovar=anteBayesB[[7]]
G1=anteBayesB[[8]][1] #add G1
def=anteBayesB[[9]]
scalea=anteBayesB[[10]]
Pi_SNP=anteBayesB[[11]]
alphacheck=anteBayesB[[12]][1]
SNPg=anteBayesB[[13]]
assoct=anteBayesB[[14]]
if(!is.null(ante_p)) assoct[ante_p]=0
} # "END" OF BAYESB
#####################################################################################
# sample residual variance
#####################################################################################
if(op$update_para$vare) vare = ( crossprod(ycorr) +op$priors$tau2_e )/rchisq(1,nrecords+op$priors$nu_e)
# scale parameter should be e`e + Se,
# degrees of freedom should be n + ve
#####################################################################################
# sample degrees of freedom
#####################################################################################
if (op$update_para$df)
{ #"START" OF SAMPLE THE DEGREES OF FREEDOM
SNPeff2=SNPeff*SNPeff
for (cycle in 1:10){
mhdf=metropDf(def, scalea, G1=nSNP, SNPeff2, priordf = 1, cdef , max = Inf,pi_math=pi_math)
def=as.numeric(mhdf[[1]])
alpha1=as.numeric(mhdf[[2]])
alphatot1=alphatot1+alpha1
if (cycle==10 & iter <= op$run_para$burnIn)
{
alphacheck1 = alphatot1/10;
if (alphacheck1 > .70) { cdef = cdef*1.2}
else if(alphacheck1 < .20) {cdef = cdef*.7} # tune Metropolis sampler #
alphatot1 = 0;
}
else if (cycle==10 & iter > op$run_para$burnIn)
{
alphacheck1 = alphatot1/10;
alphadef_save[itersave/op$run_para$skip] = alphacheck1
alphatot1 = 0
}
}
} # "END" OF SAMPLE THE DEGREES OF FREEDOM
#####################################################################################
# sample scale#
# based on Gelman Uniform(0,A) prior on scale parameter for "large" A.
#####################################################################################
if (op$update_para$scale && !(op$update_para$df))
{ #"START" OF SAMPLE THE SCALE PARAMETER
shape_g = 0.5*(G1*def+1)+op$priors$shape_scale
scale_g = 0.5*def*sum(1/varq[which(varq>0)])+op$priors$rate_scale
scalea=rgamma(1,shape=shape_g,rate=scale_g)
} #"END" OF SAMPLE THE SCALE PARAMETER
#####################################################################################
# sample scale usning UNIMH#
#####################################################################################
if (op$update_para$scale & op$update_para$df)
{ #"START" OF SAMPLE THE SCALE PARAMETER
SNPeff2=SNPeff*SNPeff
for (cycle in 1:10){
mhscale=metropScale(def, scalea, G1=G1, SNPeff2, cscalea,op$priors$shape_scale, op$priors$rate_scale, pi_math=pi_math)
scalea=as.numeric(mhscale[[1]])
alpha2=as.numeric(mhscale[[2]])
alphatot2=alphatot2+alpha2
if (cycle==10 & iter <= op$run_para$burnIn)
{
alphacheck2 = alphatot2/10;
if (alphacheck2 > .70) { cscalea = cscalea*1.2}
else if(alphacheck2 < .20) {cscalea = cscalea*.7} # tune Metropolis sampler #
alphatot2 = 0;
}
else if (cycle==10 & iter > op$run_para$burnIn)
{
alphacheck2 = alphatot2/10;
alphascalea_save[itersave/op$run_para$skip] = alphacheck2
alphatot2 = 0
}
}
} #"END" OF SAMPLE THE SCALE PARAMETER
#####################################################################################
# sample pi
#####################################################################################
if (op$update_para$pi)
{
Pi_SNP = rbeta(1,op$priors$alphapi+G1,op$priors$betapi+nSNP-G1)
} #"END" OF SAMPLE THE PI PARAMETER
#####################################################################################
# sample mean and variance for association parameters
#####################################################################################
if (op$update_para$mut)
{
######sample mut#######
sigma2hat_t=1/(1/op$priors$sigma2_m_t+length(which(assoct!=0))/vart)
muhat_t=((mean(assoct[which(assoct!=0)])*length(which(assoct!=0))/vart)+(op$priors$mu_m_t/op$priors$sigma2_m_t))*sigma2hat_t
mut = rnorm(1,mean=muhat_t,sd=sqrt(sigma2hat_t))
}
########sample vart#######################
if(op$update_para$vart)
{
vart = ( t(assoct[which(assoct!=0)]-mut)%*%(assoct[which(assoct!=0)]-mut)+1)/rchisq(1,(length(assoct[which(assoct!=0)])-1)+3)
}
if (itersave>1)
{ # "START" OF COMPUTE POSTERIOR MEANS & VARIANCES
Mcurrent = Mlast + (theta-Mlast)/itersave
Qcurrent = Qlast + (itersave-1)*((theta-Mlast)^2)/itersave
Mlast = Mcurrent
Qlast = Qcurrent
SNPvar=SNPvarlast + (varq-SNPvarlast)/itersave
SNPvarlast=SNPvar
Tcurrent=Tlast+ (assoct-Tlast)/itersave
Tlast=Tcurrent
TQcurrent= TQlast+(itersave-1)*((assoct-Tlast)^2)/itersave
TQlast=TQcurrent
if(GWA=="Win"){
Wpostprob=Wpostprob+calc.phiw(win,p1)
n_saved_sample=n_saved_sample+1
}
} # "END" OF COMPUTE POSTERIOR MEANS & VARIANCES
if (itersave==1) {
Mlast = theta;Tlast= assoct;SNPvarlast=varq;TQlast=rep(vart,length(assoct))
Wpostprob=rep(0,length(win))
n_saved_sample=0
}
###########################################################################################
#store MCMC samples at every "skip"
###########################################################################################
if (iter%%op$run_para$skip == 0)
{
varesave[iter/op$run_para$skip] = vare
if(op$update_para$df) {defsave[iter/op$run_para$skip] = def}
if(op$update_para$scale) {scalesave[iter/op$run_para$skip] = scalea}
if(op$update_para$pi) {pisave[iter/op$run_para$skip] = Pi_SNP}
if(op$update_para$mut){mutsave[iter/op$run_para$skip]=mut}
if(op$update_para$vart){vartsave[iter/op$run_para$skip]=vart}
}
endSNP = proc.time()
timeSNP = timeSNP + (endSNP[1]-startSNP[1])
endtime = proc.time()
timetotal = endtime[1]-starttime[1]
if((op$print_mcmc$piter!=0) & iter%%op$print_mcmc$piter==0)
{
tmp=paste("iter= ",iter," vare= ",round(vare,6), "scale= ", round(scalea,6),
"pi= ",round(Pi_SNP,6),
"\ntimepercycle= ", round(timeSNP/iter,3), "estimated time left=", round(timeSNP/iter*(op$run_para$niter-iter),2),"\n",sep=" ")
if(op$print_mcmc$print_to=="screen")
{
cat(tmp)
}else{
write(tmp, file = paste("log",op$seed,".txt",sep=""),append = TRUE)
}
}
if (iter%%1000==0)
{
#############################################################################################
# PROCESSING MCMC SAMPLES
#############################################################################################
hyperparameters=c()
counth=0
hyperparameters = cbind(hyperparameters,varesave);counth=counth+1
hyperparameters = cbind(hyperparameters,scalesave) ;counth=counth+1
#if (op$update_para$scale) {hyperparameters = cbind(hyperparameters,scalesave);counth=counth+1 }
if (op$update_para$df) {hyperparameters = cbind(hyperparameters,defsave);counth=counth+1 }
if (op$update_para$pi) {hyperparameters = cbind(hyperparameters,pisave);counth=counth+1 }
if (op$update_para$mut) {hyperparameters = cbind(hyperparameters,mutsave);counth=counth+1}
if (op$update_para$vart) {hyperparameters = cbind(hyperparameters,vartsave) ;counth=counth+1}
if(counth)rownames(hyperparameters) = seq(op$run_para$skip,op$run_para$niter,op$run_para$skip)
save(iter,hyperparameters,file=paste(op$save.at, "Hyperparameters",op$seed,".RData",sep=""))
SNPtimepercycle = timeSNP/iter
timepercycle=timetotal/iter
save(iter,timepercycle,SNPtimepercycle,file=paste(op$save.at, "time",op$seed,".RData",sep=""))
}# "END" OF PROCESSING MCMC SAMPLING
if (iter%%1000==0 & itersave>1 )
{
#############################################################################################
# PROCESSING MCMC SAMPLES
#############################################################################################
postprob_est = postprob_save/itersave
if(GWA=="Win") Wpostprob_est =Wpostprob/n_saved_sample else Wpostprob_est=NULL
names(postprob_est)=colnames(Z)
meanmu= Mcurrent[1:dimX]
names(meanmu)=colnames(X)
meang= Mcurrent[(dimX+1):dimW]
varg = Qcurrent[(dimX+1):dimW]/itersave
names(meang)=names(varg)=colnames(Z)
meant= Tcurrent[1:(nSNP-1)]
vart=TQcurrent[1:(nSNP-1)]/itersave
names(meant)=names(vart)=names(meang)[-1]
save(iter,meanmu,meang,varg,SNPvar,meant,vart,postprob_est,Wpostprob_est,file = paste(op$save.at,"EffectsResults",op$seed,".RData",sep=""))
# monitor MH acceptance rates over cycle for the degrees of freedom term.
save(iter,alphadef_save,alphascalea_save,file=paste(op$save.at, "alphadef",op$seed,".RData",sep=""))
} # "END" OF PROCESSING MCMC SAMPLING
} # "END" OF MCMC SAMPLING
names_hypers=c()
names_hypers=c(names_hypers,"vare")
names_hypers = c(names_hypers,"scale")
if (op$update_para$df) {names_hypers = c(names_hypers,"df") }
if (op$update_para$pi) {names_hypers = c(names_hypers,"pi") }
if (op$update_para$mut) names_hypers = c(names_hypers,"mut")
if (op$update_para$vart) names_hypers = c(names_hypers,"vart")
sample_idx=c((op$run_para$burnIn/op$run_para$skip+1):(op$run_para$niter/op$run_para$skip))
if(length(names_hypers)>1) hyper_est=apply(hyperparameters[sample_idx,],2,mean)
else if (length(names_hypers)==1) hyper_est=mean(hyperparameters[sample_idx,])
else hyper_est=c(vare,scale)
colnames(hyperparameters)=names_hypers
if(length(names_hypers)>1) names(hyper_est)=names_hypers
BAout<-list(bhat=meanmu,ahat=meang, yhat=X0%*%meanmu+Z0%*%meang,
prob=postprob_est,Wprob=Wpostprob_est,ante_t=meant,eff_sample=effectiveSize(hyperparameters),
hypers=hyperparameters,idx=idx,hyper_est=hyper_est,train=vtrain,y=y0,map=map,win=win,
Wchr=chr,GWA=GWA,model=op$model)
class(BAout)="ba"
return(BAout)
} # END OF FUNCTION
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