Nothing
mvma.fe <- function(ys, covs, tol = 1E-10){
if(missing(ys)) stop("the argument ys is missing.")
if(missing(covs)) stop("the argument covs is missing.")
ys <- as.matrix(ys)
n <- dim(ys)[1]
p <- dim(ys)[2]
if(p < 2) stop("please directly use the univariate model.")
idx.na <- numeric(0)
for(i in 1:n){
if(all(is.na(ys[i,]))) idx.na <- c(idx.na, i)
}
if(length(idx.na) > 0){
ys <- ys[-idx.na,]
covs <- covs[-idx.na]
n <- dim(ys)[1]
}
idx.obs <- vector("list", n)
X <- vector("list", n)
ys.tilde <- vector("list", n)
covs.tilde <- vector("list", n)
for(i in 1:n){
idx.obs[[i]] <- which(!is.na(ys[i,]))
X[[i]] <- diag(p)[idx.obs[[i]],]
if(length(idx.obs[[i]]) == 1) X[[i]] <- matrix(X[[i]], nrow = 1, ncol = p)
ys.tilde[[i]] <- ys[i, idx.obs[[i]]]
covs.tilde[[i]] <- as.matrix(covs[[i]][idx.obs[[i]], idx.obs[[i]]])
}
target <- function(x){
mu <- x[1:p]
Tau <- matrix(0, nrow = p, ncol = p)
out1 <- 0
for(i in 1:n){
cov.temp <- covs.tilde[[i]] + Tau[idx.obs[[i]], idx.obs[[i]]]
y.temp <- ys.tilde[[i]]
y_mu <- y.temp - mu[idx.obs[[i]]]
y_mu <- as.matrix(y_mu)
logdet <- determinant(as.matrix(cov.temp), logarithm = TRUE)
if(logdet$sign <= 0) logdet <- -Inf else logdet <- logdet$modulus
temp <- logdet + as.numeric(t(y_mu)%*%solve(cov.temp)%*%y_mu)
out1 <- out1 + temp
}
out <- as.numeric(out1)
return(out)
}
inits <- rep(0, p)
low <- rep(-Inf, p)
upp <- rep(Inf, p)
optim.out <- optim(par = inits, fn = target, lower = low, upper = upp, method = "L-BFGS-B", control = list(factr = tol))
pars <- optim.out$par
mu.est <- pars[1:p]
Tau.est <- matrix(0, nrow = p, ncol = p)
temp <- matrix(0, p, p)
for(i in 1:n){
cov.temp <- covs.tilde[[i]] + Tau.est[idx.obs[[i]], idx.obs[[i]]]
if(length(idx.obs[[i]]) == 1){
cov.temp <- as.numeric(cov.temp)
temp <- temp + t(X[[i]])%*%X[[i]]/cov.temp
}else{
temp <- temp + t(X[[i]])%*%solve(cov.temp)%*%X[[i]]
}
}
mu.cov <- solve(temp)
out <- list(mu.est = mu.est, mu.cov = mu.cov, method = "Fixed-effects model")
return(out)
}
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