Nothing
covc <- function(ran1,ran2, thetaC=NULL, theta=NULL){
if( ncol(ran1$Z[[1]]) != ncol(ran2$Z[[1]]) ){stop("Matrices of the two random effects should have the same dimensions",call. = FALSE)}
ran1$Z[[2]] <- ran2$Z[[1]]
if(is.null(thetaC)){
ran1$thetaC <- unsm(2);
}else{ran1$thetaC <- thetaC}
ran1$thetaC[lower.tri(ran1$thetaC)] = 0 # lower.tri must be 0
colnames(ran1$thetaC) <- rownames(ran1$thetaC) <- c("ran1","ran2")
if(is.null(theta)){
ran1$theta <- diag(2) * 0.05 + matrix(0.1, 2, 2)
}else{ran1$theta <- theta}
nvc <- length(which(thetaC > 0))
ran1$thetaF <- diag(nvc) # n x n, where n is number of vc to estimate
ran1$sp <- rep(0,nvc) # rep 0 n times, where n is number of vc to estimate
return(ran1)
}
stackTrait <- function(data, traits){
idvars <- setdiff(colnames(data), traits)
dataScaled <- data
for(iTrait in traits){
dataScaled[,iTrait] <- scale(dataScaled[,iTrait])
}
data2 <- reshape(data, idvar = idvars, varying = traits,
timevar = "trait",
times = traits,v.names = "value", direction = "long")
data2Scaled <- reshape(dataScaled, idvar = idvars, varying = traits,
timevar = "trait",
times = traits,v.names = "value", direction = "long")
data2 <- as.data.frame(data2)
data2$valueS <- as.vector(unlist(data2Scaled$value))
rownames(data2) <- NULL
varG <- cov(data[,traits], use="pairwise.complete.obs")
# varG <- apply(data[,traits],2,var, na.rm=TRUE)
mu <- apply(data[,traits],2,mean, na.rm=TRUE)
return(list(long=data2, varG=varG, mu=mu))
}
add.diallel.vars <- function(df, par1="Par1", par2="Par2",sep.cross="-"){
# Dummy variables for selfs, crosses, combinations
df[,"is.cross"] <- ifelse(df[,par1] == df[,par2], 0, 1)
df[,"is.self"] <- ifelse(df[,par1] == df[,par2], 1, 0)
df[,"cross.type"] <- ifelse(as.character(df[,par1]) < as.character(df[,par2]), -1,
ifelse(as.character(df[,par1]) == as.character(df[,par2]), 0, 1))
# Dummy variable for the combinations, ignoring the reciprocals
df[,"cross.id"]<-factor(ifelse(as.character(df[,par1]) <= as.character(df[,par2]),
paste(df[,par1], df[,par2], sep =sep.cross),
paste(df[,par2], df[,par1], sep =sep.cross)) )
return(df)
}
overlay<- function (..., rlist = NULL, prefix = NULL, sparse=FALSE){
init <- list(...) # init <- list(DT$femalef,DT$malef)
## keep track of factor variables
myTypes <- unlist(lapply(init,class))
init0 <- init
##
init <- lapply(init, as.character)
namesInit <- as.character(substitute(list(...)))[-1L] # names <- c("femalef","malef")
dat <- as.data.frame(do.call(cbind, init))
dat <- as.data.frame(dat)
## bring back the levels
for(j in 1:length(myTypes)){
if(myTypes[j]=="factor"){
levels(dat[,j]) <- c(levels(dat[,j]),setdiff(levels(init0[[j]]),levels(dat[,j]) ))
}
}
##
if (is.null(dim(dat))) {
stop("Please provide a data frame to the overlay function, not a vector.\\n",
call. = FALSE)
}
if (is.null(rlist)) {
rlist <- as.list(rep(1, dim(dat)[2]))
}
ss1 <- colnames(dat)
dat2 <- as.data.frame(dat[, ss1])
head(dat2)
colnames(dat2) <- ss1
femlist <- list()
S1list <- list()
for (i in 1:length(ss1)) {
femlist[[i]] <- ss1[i]
dat2[, femlist[[i]]] <- as.factor(dat2[, femlist[[i]]])
if(sparse){
S1 <- Matrix::sparse.model.matrix(as.formula(paste("~", femlist[[i]],
"-1")), dat2)
}else{
S1 <- model.matrix(as.formula(paste("~", femlist[[i]],
"-1")), dat2)
}
colnames(S1) <- gsub(femlist[[i]], "", colnames(S1))
S1list[[i]] <- S1
}
levo <- sort(unique(unlist(lapply(S1list, function(x) {
colnames(x)
}))))
if(sparse){
S3 <- Matrix(0, nrow = dim(dat2)[1], ncol = length(levo))
}else{
S3 <- matrix(0, nrow = dim(dat2)[1], ncol = length(levo))
}
rownames(S3) <- rownames(dat2)
colnames(S3) <- levo
for (i in 1:length(S1list)) {
if (i == 1) {
S3[rownames(S1list[[i]]), colnames(S1list[[i]])] <- S1list[[i]] *
rlist[[i]]
}
else {
S3[rownames(S1list[[i]]), colnames(S1list[[i]])] <- S3[rownames(S1list[[i]]),
colnames(S1list[[i]])] + (S1list[[i]][rownames(S1list[[i]]),
colnames(S1list[[i]])] * rlist[[i]])
}
}
if (!is.null(prefix)) {
colnames(S3) <- paste(prefix, colnames(S3), sep = "")
}
attr(S3,"variables") <- namesInit
return(S3)
}
list2usmat <- function(sigmaL){
f <- function(n, x){
res <- ((n*(n-1))/2 + n) - x
if(res < 0){res <- 100}
return(res)
}
if(is.list(sigmaL)){
ss <- unlist(sigmaL)
}else{ss <- sigmaL}
x <- length(ss)
n <- round(optimize(f, c(1, 50), tol = 0.0001, x=x)$minimum)
mss <- matrix(NA,n,n)
mss[upper.tri(mss,diag = TRUE)] <- ss
mss[lower.tri(mss)] <- t(mss[upper.tri(mss)])
return(mss)
}
replace.values <- function(Values,Search,Replace){
dd0 <- data.frame(Values)
vv <- which(Values%in%Search)
dd <- data.frame(Search,Replace)
rownames(dd) <- Search
dd0[vv,"Values"] <- as.character(dd[Values[vv],"Replace"])
return(dd0[,1])
}
myformula <- function(x){
expi <- function(j){gsub("[\\(\\)]", "", regmatches(j, gregexpr("\\(.*?\\)", j))[[1]])}
expi2 <- function(x){gsub("(?<=\\()[^()]*(?=\\))(*SKIP)(*F)|.", "", x, perl=T)}
yuyuf <- strsplit(as.character(x[3]), split = "[+]")[[1]]
termss <- apply(data.frame(yuyuf),1,function(x){
strsplit(as.character((as.formula(paste("~",x)))[2]), split = "[+]")[[1]]
})
newtermss <- apply(data.frame(yuyuf),1,function(y){
newy <- expi(y)
if(length(newy) > 0){
newy <- gsub(",.*","",newy)
}else{newy <- y}
return(newy)
})
resp <- strsplit(as.character(x[2]), split = "[+]")[[1]]
newx <- paste(resp, "~",paste(newtermss,collapse = "+"))
return(newx)
}
reshape_mmer <- function(object, namelist){
nt <- nrow(as.matrix(object$sigma[,,1]))
ntn <- attr(object$sigma, "dimnames")[[2]]
nre <- length(object$U)
nren <- attr(object$sigma, "dimnames")[[3]]
U2 <- list()#vector("list",nre)
VarU2 <- list()
PevU2 <- list()
if(nre > 0){
for(ire in 1:nre){
N <- nrow(object$U[[ire]])
utlist <- list()# vector("list",nt)
varutlist <- list()# vector("list",nt)
pevutlist <- list()# vector("list",nt)
# pick <- numeric()
# for(it in 1:nt){
# pick <- c(pick,seq(it,N,nt))
# }
provus <- matrix(object$U[[ire]],ncol=nt,byrow = T)
for(it in 1:nt){
pick <- seq(it,N,nt)
pit <- ntn[it]
utlist[[pit]] <- provus[,it] # object$U[[ire]][pick,]
names(utlist[[ pit ]]) <- namelist[[ire]]
# print(length(object$VarU[[ire]]))
if(length(object$VarU[[ire]]) > 0){
varutlist[[ pit ]] <- as.matrix(object$VarU[[ire]][pick,pick])
rownames(varutlist[[ pit ]]) <- colnames(varutlist[[ pit ]]) <- namelist[[ire]]
}else{varutlist[[ pit ]] <- varutlist[[ pit ]] }
if(length(object$PevU[[ire]]) > 0){
pevutlist[[ pit ]] <- as.matrix(object$PevU[[ire]][pick,pick])
rownames(pevutlist[[ pit ]]) <- colnames(pevutlist[[ pit ]]) <- namelist[[ire]]
}else{pevutlist[[ pit ]] <-pevutlist[[ pit ]]}
}
U2[[ nren[ire] ]] <- utlist
VarU2[[ nren[ire] ]] <- varutlist
PevU2[[ nren[ire] ]] <- pevutlist
}
object$U <- U2; U2<-NULL
object$VarU <- VarU2; VarU2<-NULL
object$PevU <- PevU2; PevU2<-NULL
}
N <- nrow(object$Vi)
pick <- numeric()
for(it in 1:nt){
pick <- c(pick,seq(it,N,nt))
}
object$Vi <- object$Vi[pick,pick]
# object$constraintsF
# object$Beta <- matrix(object$Beta,ncol=nt,byrow = F)
# print(namelist[[length(namelist)]])
# print(object$Beta)
# rownames(object$Beta) <- namelist[[length(namelist)]]
object$Beta <- cbind(namelist[[length(namelist)]],object$Beta)
colnames(object$Beta) <- c("Trait","Effect","Estimate")
object$fitted <- matrix(object$fitted,ncol=nt, byrow=TRUE)
object$residuals <- matrix(object$residuals,ncol=nt, byrow = TRUE)
MyArray <- object$sigma
object$sigma <- lapply(seq(dim(MyArray)[3]), function(x) MyArray[ , , x])
object$sigma <- lapply(object$sigma,function(x){mm <- as.matrix(x);colnames(mm)<-rownames(mm) <- ntn;return(mm)})
names(object$sigma) <- nren
MyArray <- object$sigma_scaled
object$sigma_scaled <- lapply(seq(dim(MyArray)[3]), function(x) MyArray[ , , x])
object$sigma_scaled <- lapply(object$sigma_scaled,function(x){mm <- as.matrix(x);colnames(mm)<-rownames(mm) <- ntn;return(mm)})
names(object$sigma_scaled) <- nren
# colnames(object$Beta) <- ntn
return(object)
}
##############
## na.methods
subdata <- function(data,fixed,na.method.Y=NULL,na.method.X=NULL){
# silently change all columns that are defined as character into factors
# columnTypes <- unlist(lapply(data, class))
# columnTypesC <- which(columnTypes == "character")
# if(length(columnTypesC) > 0){ # if there's character types change them to factor
# for(cti in 1:length(columnTypesC)){
# data[,cti] <- as.factor(data[,cti])
# }
# }
####
expi <- function(j){gsub("[\\(\\)]", "", regmatches(j, gregexpr("\\(.*?\\)", j))[[1]])}
expi2 <- function(x){gsub("(?<=\\()[^()]*(?=\\))(*SKIP)(*F)|.", "", x, perl=T)}
response <- strsplit(as.character(fixed[2]), split = "[+]")[[1]]
responsef <- as.formula(paste(response,"~1"))
mfna <- try(model.frame(responsef, data = data, na.action = na.pass), silent = TRUE)
if (is(mfna, "try-error") ) { # class(mfna) == "try-error"
stop("Please provide the 'data' argument for your specified variables.\nYou may be specifying some variables in your model not present in your dataset.", call. = FALSE)
}
mfna <- eval(mfna, parent.frame())
yvar <- as.matrix(model.response(mfna))
nt <- ncol(yvar)
good <- 1:nrow(data)
if(nt==1){colnames(yvar) <- response}
if(na.method.Y=="include"){
touse <- colnames(yvar)
for(i in 1:length(touse)){
use <- touse[i]
# print(iname)
data[,use] <- imputev(data[,use])
}
}else if(na.method.Y=="include2"){
tlist <- list()
touse <- colnames(yvar)
for(i in 1:length(touse)){
# print(touse[i])
use <- touse[i]
vivi <- as.vector(data[,use])
tlist[[i]] <- which(!is.na(vivi))
}
# print(tlist)
good <- sort(unique(unlist(tlist)))
data <- data[good,]
for(i in 1:length(touse)){
use <- touse[i]
# print(iname)
data[,use] <- imputev(data[,use])
}
}else if(na.method.Y=="exclude"){
tlist <- list()
touse <- colnames(yvar)
for(i in 1:length(touse)){
# print(touse[i])
use <- touse[i]
vivi <- as.vector(data[,use])
tlist[[i]] <- which(!is.na(vivi))
}
# print(tlist)
if(length(tlist)==1){ #only one trait
good <- tlist[[1]]
}else{#more than one trait
good <- Reduce(intersect,tlist)
}
data <- data[good,]
}else{stop("na.method.Y not recognized")}
data <- data.frame(data)
##########
## na.method x
yuyu <- strsplit(as.character(fixed[3]), split = "[+]")[[1]]
xtermss <- apply(data.frame(yuyu),1,function(x){
strsplit(as.character((as.formula(paste("~",x)))[2]), split = "[+]")[[1]]
})
xtermss2 <- apply(data.frame(xtermss),1,function(x){gsub(",.*","",expi2(x))})
xtermss2[which(xtermss2 == "")] <- xtermss[which(xtermss2 == "")]
xtermss2 <- intersect(colnames(data),xtermss2) # only focus on the terms that are in teh dataset so we can skip overlay and weird vs structures
# print(xtermss2)
if(length(xtermss2) > 0){
mycl <- as.vector(unlist(lapply(data.frame(data[,xtermss2]),class)))
if(na.method.X=="include"){
touse <- xtermss2
for(i in 1:length(touse)){
use <- touse[i]
usecl <- mycl[i]
if(usecl == "factor"){data[,use] <- as.factor(imputev(data[,use]))}else{data[,use] <- imputev(data[,use])}
}
}else if(na.method.X=="exclude"){
tlist <- list()
touse <- xtermss2
for(i in 1:length(touse)){
# print(touse[i])
use <- touse[i]
vivi <- as.vector(data[,use])
tlist[[i]] <- which(!is.na(vivi))
}
# print(tlist)
if(length(tlist)==1){ #only one trait
good <- tlist[[1]]
}else{#more than one trait
good <- Reduce(intersect,tlist)
}
data <- data[good,]
}else{stop("na.method.Y not recognized")}
data <- data.frame(data)
}
return(list(datar=data,good=good))
}
##############
## VS structures
atr <- function(x, levs){
if(is.matrix(x)){
dummy <- x
m0 <- rep(0,ncol(dummy))
names(m0) <- levels(as.factor(colnames(dummy)))#as.character(unique(x))
if(missing(levs)){levs <- names(m0)}
m0[levs] <- 1
mm <- diag(m0)
colnames(mm) <- rownames(mm) <- colnames(dummy)
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- matrix(x,ncol=1); colnames(dummy) <- namess
m0 <- rep(0,ncol(dummy))
names(m0) <- levels(as.factor(colnames(dummy)))#as.character(unique(x))
if(missing(levs)){levs <- names(m0)}
m0[levs] <- 1
mm <- diag(m0)
colnames(mm) <- rownames(mm) <- colnames(dummy)
}else{
dummy <- x
dummy <- model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
m0 <- rep(0,ncol(dummy))
names(m0) <- levels(as.factor(colnames(dummy)))#as.character(unique(x))
if(missing(levs)){levs <- names(m0)}
m0[levs] <- 1
mm <- diag(m0)
colnames(mm) <- rownames(mm) <- colnames(dummy)
}
}
return(list(Z=dummy,thetaC=mm))
}
csr <- function(x,mm){
if(is.matrix(x)){
mm <- mm
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- matrix(x,ncol=1); colnames(dummy) <- namess
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
}
mm <- mm
}
# mm[lower.tri(mm)] <- 0
colnames(mm) <- rownames(mm) <- colnames(dummy)
return(list(Z=dummy,thetaC=mm))
}
dsr <- function(x){
if(is.matrix(x)){
dummy <- x
mm <- diag(1,ncol(x))
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- matrix(x,ncol=1); colnames(dummy) <- namess
mm <- diag(ncol(dummy));
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
mm <- diag(1,ncol(dummy))
}
}
colnames(mm) <- rownames(mm) <- colnames(dummy)
return(list(Z=dummy,thetaC=mm))
}
usr <- function(x){
# namx <- as.character(substitute(list(x)))[-1L]
if(is.matrix(x)){
dummy <- x
mm <- unsm(ncol(dummy))
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- matrix(x,ncol=1); colnames(dummy) <- namess
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
}
mm <- unsm(ncol(dummy))
}
colnames(mm) <- rownames(mm) <- colnames(dummy)
return(list(Z=dummy,thetaC=mm))
}
###############
## VS structures for mmec
redmm <- function (x, M = NULL, Lam=NULL, nPC=50, cholD=FALSE, returnLam=FALSE) {
if(system.file(package = "RSpectra") == ""){
stop("Please install the RSpectra package to use the redmm() function.",call. = FALSE)
}else{
requireNamespace("RSpectra",quietly=TRUE)
}
if(is.null(M)){
stop("M cannot be NULL. We need a matrix of features that defines the levels of x")
}else{
if (inherits(x, "dgCMatrix") | inherits(x, "matrix")) {
notPresentInM <- setdiff(colnames(Z),rownames(M))
notPresentInZ <- setdiff(rownames(M),colnames(x))
}else{
notPresentInM <- setdiff(unique(x),rownames(M))
notPresentInZ <- setdiff(rownames(M),unique(x))
}
if(is.null(Lam)){ # user didn't provide a Lambda matrix
if(nPC == 0){ # user wants to use the full marker matrix
Lam <- Lam0 <- M
}else{ # user wants to use the PCA method
nPC <- min(c(nPC, ncol(M)))
if(cholD){
smd <- try(chol(M) , silent = TRUE)
if(inherits(smd, "try-error")){smd <- try(chol((M+diag(1e-5,nrow(M),nrow(M))) ) , silent = TRUE)}
Lam0 = t(smd)
}else{
smd <- RSpectra::svds(M, k=nPC, which = "LM")
Lam0 <- smd$u
}
Lam = Lam0[,1:min(c(nPC,ncol(M))), drop=FALSE]
rownames(Lam) <- rownames(M)
colnames(Lam) <- paste0("nPC",1:nPC)
}
}else{ # user provided it's own Lambda matrix
Lam0=Lam
Lam = Lam0[,1:min(c(nPC,ncol(M))), drop=FALSE]
rownames(Lam) <- rownames(M)
colnames(Lam) <- paste0("nPC",1:nPC)
}
}
if (inherits(x, "dgCMatrix") | inherits(x, "matrix")) {
Z <- x
}else{
if (!is.character(x) & !is.factor(x)) {
namess <- as.character(substitute(list(x)))[-1L]
Z <- Matrix(x, ncol = 1)
colnames(Z) <- namess
}else {
dummy <- x
levs <- na.omit(unique(dummy))
if (length(levs) > 1) {
Z <- Matrix::sparse.model.matrix(~dummy - 1, na.action = na.pass)
colnames(Z) <- gsub("dummy", "", colnames(Z))
} else {
vv <- which(!is.na(dummy))
Z <- Matrix(0, nrow = length(dummy))
Z[vv, ] <- 1
colnames(Z) <- levs
}
}
}
Zstar <- as.matrix(Z %*% Lam[colnames(Z),])
if(returnLam){
return(list(Z = Zstar, Lam=Lam, Lam0=Lam0))
}else{return(Zstar)}
}
rrc <- function(timevar=NULL, idvar=NULL, response=NULL,
Gu=NULL, nPC=2, returnGamma=FALSE, cholD=TRUE){
if(is.null(timevar)){stop("Please provide the timevar argument.", call. = FALSE)}
if(is.null(idvar)){stop("Please provide the idvar argument.", call. = FALSE)}
if(is.null(response)){stop("Please provide the response argument.", call. = FALSE)}
# these are called PC models by Meyer 2009, GSE. This is a reduced rank implementation
# we produce loadings, the Z*L so we can use it to estimate factor scores in mmec()
dtx <- data.frame(timevar=timevar, idvar=idvar, v.names=response)
dtx2 <- aggregate(v.names~timevar+idvar, data=dtx, FUN=mean, na.rm=TRUE)
wide <- reshape(dtx2, direction = "wide", idvar = "idvar",
timevar = "timevar", v.names = "v.names", sep= "_")
rowNamesWide <- wide[,1]
rownames(wide) <- rowNamesWide
wide <- wide[,-1]
# if user doesn't provide the a Gu we impute simply and use the correlation matrix as a Gu
if(is.null(Gu)){
X <- apply(wide, 2, sommer::imputev)
Gu <- cor(t(X))
}else{
Gu = cov2cor(Gu)
}
# impute missing data using a relationship matrix
if(is.null(rownames(Gu))){stop("Gu needs to have row names.", call. = FALSE)}
if(is.null(colnames(Gu))){stop("Gu needs to have column names.", call. = FALSE)}
for(iEnv in 1:ncol(wide)){ # iEnv=1
withData <- which(!is.na(wide[,iEnv]))
withoutData <- which(is.na(wide[,iEnv]))
imputationVector <- as.numeric(Gu[as.character(rowNamesWide),as.character(rowNamesWide[withData])] %*% as.matrix(wide[withData,iEnv]))
wide[,iEnv] <- imputationVector # wide[withoutData,iEnv] <- imputationVector[withoutData]
# scaleFactor=imputationVector[withData[1]] / wide[withData[1],iEnv]
}
##
Y <- apply(wide,2, sommer::imputev)
Sigma <- cov(scale(Y, scale = TRUE, center = TRUE)) # surrogate of unstructured matrix to start with
Sigma <- as.matrix(nearPD(Sigma)$mat)
# GE <- as.data.frame(t(scale( t(scale(Y, center=T,scale=F)), center=T, scale=F))) # sum(GE^2)
if(cholD){
## OPTION 2. USING CHOLESKY
Gamma <- t(chol(Sigma)); # LOADINGS # same GE=LL' from cholesky plot(unlist(Gamma%*%t(Gamma)), unlist(GE))
}else{
## OPTION 1. USING SVD
U <- svd(Sigma)$u; # V <- svd(GE)$v
D <- diag(svd(Sigma)$d)
Gamma <- U %*% sqrt(D); # LOADINGS
rownames(Gamma) <- colnames(Gamma) <- rownames(Sigma)
}
colnamesGamma <- colnames(Gamma)
rownamesGamma <- rownames(Gamma)
Gamma <- as.matrix(Gamma[,1:nPC]);
colnames(Gamma) <- colnamesGamma[1:nPC]
rownames(Gamma) <- rownamesGamma
##
rownames(Gamma) <- gsub("v.names_","",rownames(Gamma))#rownames(GE)#levels(dataset$Genotype); # rownames(Se) <- colnames(GE)#levels(dataset$Environment)
colnames(Gamma) <- paste("PC", 1:ncol(Gamma), sep =""); #
######### GEreduced = Sg %*% t(Se)
# if we want to merge with PCs for environments
dtx$index <- 1:nrow(dtx)
dtx2 <- dtx[which(!is.na(dtx$v.names)),]
Z <- Matrix::sparse.model.matrix(~timevar -1, na.action = na.pass, data=dtx2)
colnames(Z) <- gsub("timevar","",colnames(Z))
Z <- Z%*%Gamma[colnames(Z),] # we multiple original Z by the LOADINGS
Z <- as.matrix(Z)
rownames(Z) <- NULL
if(returnGamma){
return(list(Gamma=Gamma, wide=wide))
}else{
return(Z)
}
}
atc <- function(x, levs, thetaC=NULL, theta=NULL){
if(is.matrix(x)){
dummy <- x
dummy <- dummy[,levs]
m0 <- rep(0,ncol(dummy))
names(m0) <- levels(as.factor(colnames(dummy)))#as.character(unique(x))
if(missing(levs)){levs <- names(m0)}
m0[levs] <- 1
mm <- Diagonal(m0)
colnames(mm) <- rownames(mm) <- colnames(dummy)
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- Matrix(x,ncol=1); colnames(dummy) <- namess
dummy <- dummy[,levs]
m0 <- rep(0,ncol(dummy))
names(m0) <- levels(as.factor(colnames(dummy)))#as.character(unique(x))
if(missing(levs)){levs <- names(m0)}
m0[levs] <- 1
mm <- Diagonal(m0)
colnames(mm) <- rownames(mm) <- colnames(dummy)
}else{
dummy <- x
dummy <- Matrix::sparse.model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
dummy <- dummy[,levs, drop=FALSE]
m0 <- rep(0,ncol(dummy))
names(m0) <- levels(as.factor(colnames(dummy)))#as.character(unique(x))
if(missing(levs)){levs <- names(m0)}
m0[levs] <- 1
mm <- diag(m0)
colnames(mm) <- rownames(mm) <- colnames(dummy)
}
}
bnmm <- mm*0.15
if(nrow(bnmm) > 1){
bnmm[upper.tri(bnmm)]=bnmm[upper.tri(bnmm)]/2
}
if(!is.null(thetaC)){
mm <- thetaC
}
if(!is.null(theta)){
bnmm <- theta
}
mm[lower.tri(mm)]=0
return(list(Z=dummy,thetaC=mm, theta=bnmm))
}
csc <- function(x,mm, thetaC=NULL, theta=NULL){
if(is.matrix(x)){
mm <- mm
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- Matrix(x,ncol=1); colnames(dummy) <- namess
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- Matrix::sparse.model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
}
mm <- mm
}
# mm[lower.tri(mm)] <- 0
colnames(mm) <- rownames(mm) <- colnames(dummy)
bnmm <- mm*0.15
if(nrow(bnmm) > 1){
bnmm[upper.tri(bnmm)]=bnmm[upper.tri(bnmm)]/2
}
if(!is.null(thetaC)){
mm <- thetaC
}
if(!is.null(theta)){
bnmm <- theta
}
mm[lower.tri(mm)]=0
return(list(Z=dummy,thetaC=mm,theta=bnmm))
}
dsc <- function(x, thetaC=NULL, theta=NULL){
if(is.matrix(x)){
dummy <- x
mm <- diag(1,ncol(x))
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- as(Matrix(x,ncol=1), Class = "dgCMatrix"); colnames(dummy) <- namess
mm <- diag(ncol(dummy));
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- Matrix::sparse.model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
mm <- diag(1,ncol(dummy))
}
}
colnames(mm) <- rownames(mm) <- colnames(dummy)
bnmm <- mm*0.15
if(nrow(bnmm) > 1){
bnmm[upper.tri(bnmm)]=bnmm[upper.tri(bnmm)]/2
}
if(!is.null(thetaC)){
mm <- thetaC
}
if(!is.null(theta)){
bnmm <- theta
}
mm[lower.tri(mm)]=0
return(list(Z=dummy,thetaC=mm, theta=bnmm))
}
usc <- function(x, thetaC=NULL, theta=NULL){
# namx <- as.character(substitute(list(x)))[-1L]
if(is.matrix(x)){
dummy <- x
mm <- unsm(ncol(dummy))
}else{
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- Matrix(x,ncol=1); colnames(dummy) <- namess
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- Matrix::sparse.model.matrix(~dummy-1,na.action = na.pass)
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- Matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
}
mm <- unsm(ncol(dummy))
}
colnames(mm) <- rownames(mm) <- colnames(dummy)
bnmm <- diag(ncol(mm))*.05 + matrix(.1,ncol(mm),ncol(mm))
if(!is.null(thetaC)){
mm <- thetaC
}
if(!is.null(theta)){
bnmm <- theta
}
mm[lower.tri(mm)]=0
return(list(Z=dummy,thetaC=mm,theta=bnmm))
}
isc <- function(x, thetaC=NULL, theta=NULL){
if(class(x)[1] %in% c("dgCMatrix","matrix") ){
dummy <- as(x, Class="dgCMatrix")
mm <- diag(1)#,ncol(x))
}else{ # if user provides a vector
if(!is.character(x) & !is.factor(x)){
namess <- as.character(substitute(list(x)))[-1L]
dummy <- Matrix(x,ncol=1); colnames(dummy) <- namess
mm <- diag(1);
}else{
dummy <- x
levs <- na.omit(unique(dummy))
if(length(levs) > 1){
dummy <- Matrix::sparse.model.matrix(~dummy-1,na.action = na.pass)
if(!is(class(dummy), "dgCMatrix")){
dummy <- as(dummy, Class="dgCMatrix")
}
colnames(dummy) <- gsub("dummy","",colnames(dummy))
}else{
vv <- which(!is.na(dummy));
dummy <- Matrix(0,nrow=length(dummy))
dummy[vv,] <- 1; colnames(dummy) <- levs
}
mm <- diag(1)
}
}
colnames(mm) <- rownames(mm) <- "isc"# colnames(dummy)
bnmm <- mm*.15
if(nrow(bnmm) > 1){
bnmm[upper.tri(bnmm)]=bnmm[upper.tri(bnmm)]/2
}
if(!is.null(thetaC)){
mm <- thetaC
}
if(!is.null(theta)){
bnmm <- theta
}
mm[lower.tri(mm)]=0
return(list(Z=dummy,thetaC=mm, theta=bnmm))
}
###############
## small matrix constructors
unsm <- function(x, reps=NULL){
mm <- matrix(1,x,x)
mm[upper.tri(mm)] <- 2
mm[lower.tri(mm)] <- 2
if(!is.null(reps)){
return(rep(list(mm),reps))
}else{return(mm)}
}
fixm <- function(x, reps=NULL){
mm <- matrix(3,x,x)
if(!is.null(reps)){
return(rep(list(mm),reps))
}else{return(mm)}
}
fcm <- function(x, reps=NULL){
mm <- diag(x)
mm <- mm[,which(apply(mm,2,sum) > 0)]
mm <- as.matrix(mm)
if(!is.null(reps)){
return(rep(list(mm),reps))
}else{return(mm)}
}
bivariateRun <- function(model, n.core=1){
args <- model[[length(model)]]
if(!args$return.param){
stop("The model provided needs to have the return.param argument set to TRUE. \nPlease read the documentation of the bivariateRun function carefully.\n", call. = FALSE)
}
response <- strsplit(as.character(args$fixed[2]), split = "[+]")[[1]]
expi <- function(j){gsub("[\\(\\)]", "", regmatches(j, gregexpr("\\(.*?\\)", j))[[1]])}
expi2 <- function(x){gsub("(?<=\\()[^()]*(?=\\))(*SKIP)(*F)|.", "", x, perl=T)}
traits <- trimws(strsplit(expi(response),",")[[1]])
combos <- expand.grid(traits,traits)
combos <- combos[which(combos[,1] != combos[,2]),];
combos <- combos[!duplicated(t(apply(combos, 1, sort))),];rownames(combos) <- NULL
RHS <- as.character(args$fixed[3])
it <- as.list(1:nrow(combos))
cat(paste(nrow(combos), "bivariate models to be run\n"))
model.results <- parallel::mclapply(it,
function(x) {
# score.calc(M[ix.pheno, markers])
ff <- as.formula(paste("cbind(",paste(as.vector(unlist(combos[x,])),collapse = ","),") ~", RHS))
# do the fixed call
args2 <- args; args2$fixed <- ff; args2$return.param <- FALSE
# modify the random call for 2 traits
p1 <- gsub("unsm\\([[:digit:]])","unsm(2)",as.character(args2$random))
p1 <- gsub("diag\\([[:digit:]])","diag(2)",p1)
p1 <- gsub("uncm\\([[:digit:]])","uncm(2)",p1)
args2$random <- as.formula(paste(p1[1],paste(p1[-1],collapse = "+")))
# modify the rcov call for 2 traits
p1 <- gsub("unsm\\([[:digit:]])","unsm(2)",as.character(args2$rcov))
p1 <- gsub("diag\\([[:digit:]])","diag(2)",p1)
p1 <- gsub("uncm\\([[:digit:]])","uncm(2)",p1)
args2$rcov <- as.formula(paste(p1[1],paste(p1[-1],collapse = "+")))
gsub("[1-9]","k",as.character(args2$random))
res0 <- do.call(mmer, args=args2)
return(res0)
},
mc.cores = n.core)
sigmas <- lapply(model.results, function(x){x$sigma})
sigmas_scaled <- lapply(model.results, function(x){x$sigma_scaled})
nre <- length(sigmas[[1]])
namesre <- names(model.results[[1]]$sigma)
sigmaslist <- list()
sigmas_scaledlist <- list()
for(i in 1:nre){
mt <- matrix(0,length(traits),length(traits))
rownames(mt) <- colnames(mt) <- traits
mts <- mt
sigmaprov <- lapply(sigmas, function(x){x[[i]]})
sigmascaledprov <- lapply(sigmas_scaled, function(x){x[[i]]})
for(j in 1:length(sigmaprov)){
mt[colnames(sigmaprov[[j]]),colnames(sigmaprov[[j]])] <- sigmaprov[[j]]
mts[colnames(sigmascaledprov[[j]]),colnames(sigmascaledprov[[j]])] <- sigmascaledprov[[j]]
}
sigmaslist[[namesre[i]]] <- mt
sigmas_scaledlist[[namesre[i]]] <- mts
}
names(model.results) <- apply(combos,1,function(x){paste(x,collapse = "-")})
# corlist <- lapply(sigmaslist,cov2cor)
final <- list(sigmas=sigmaslist, sigmas_scaled=sigmas_scaledlist, models=model.results)
return(final)
}
transformConstraints <- function(list0,value=1){
ll <- lapply(list0, function(x){
x[which(x != 0,arr.ind = TRUE)] <- x[which(x != 0,arr.ind = TRUE)] / x[which(x != 0,arr.ind = TRUE)]
x <- x*value
return(x)
})
return(ll)
}
# unsBLUP <- function(blups){
# l <- unlist(lapply(blups,function(x){length(x[[1]])}))
# lmin <- min(l); lmax <- max(l)
# indexCov1 <- 1:lmin
# indexCov2 <- (lmin+1):lmax
# ntraits <- length(blups[[1]])
# # blups follow the order of a lower triangula matrix
# # (n*(n-1))/2 = l
# # l*2 = n2 - n
# # n2 = l*2 - n
# n <- 1:100
# possibilities <- ((n*(n-1))/2) + n
# ntrue <- n[which(possibilities == length(l))]
# ## index to know how to add them up
# base <- matrix(NA,ntrue,ntrue)
# base[lower.tri(base, diag=TRUE)] <- 1:length(l)
# index <- which(!is.na(base), arr.ind = TRUE)
# index <- index[order(index[,1]), ]
#
#
# for(i in 1:ntrue){ # for each main blup
# main <- which(index[,1] == i & index[,2] == i, arr.ind = TRUE)
# cov1 <- which(index[,1] == i & index[,2] != i, arr.ind = TRUE)
# cov2 <- which(index[,1] != i & index[,2] == i, arr.ind = TRUE)
# for(itrait in 1:ntraits){
# start <- blups[[main]][[itrait]]
# for(icov1 in cov1){
# start <- start + blups[[icov1]][[itrait]][indexCov1]
# }
# for(icov2 in cov2){
# start <- start + blups[[icov2]][[itrait]][indexCov2]
# }
# # store adjusted blup adding covariance effects in the same structure
# blups[[main]][[itrait]] <- start
# }
# }
# return(blups)
# }
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