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getModelComponents <- function(m, analytic) UseMethod("getModelComponents")
getModelComponents.lme <-
function(m, analytic = TRUE) {
model <- list()
model$df <- NULL
X <- model.matrix(formula(m),m$data)
n <- nrow(X)
Z <- as.matrix(get_Z(m))
theta <- get_theta(m)
Lambdat <- get_LambdaT(m)$LambdaT
D <- get_LambdaT(m)$D
Lambda <- t(Lambdat)
model$Wlist <- list()
model$eWelist <- list()
# L <- get_L(m)
sig2 <- sigma(m)^2
R <- get_R(m) / sig2 # definition according to derivation of bc with weights
# w <- sig2 / diag(get_R(m))
Rinv <- solve(R)
model$R <- R
Zt <- t(Z)
#Dinv <- solve(get_LambdaT(m)$D)
V0inv <- solve(Matrix(Z %*% D %*% Zt + get_R(m))/sig2)
RX <- get_RX(m)
A <- V0inv - crossprod(crossprod(X %*% solve(RX), V0inv))
y <- as.vector(getResponse(m))
e <- residuals(m)
## prepare list of derivative matrices W_j
ind <- get_Lind(m)
len <- rep(0, length(Lambda@x))
for (s in 1:length(theta)) {
# model$Wlist <- lapply(theta, function(s){
LambdaS <- Lambda
LambdaSt <- Lambdat
LambdaS@x <- LambdaSt@x <- len
LambdaS@x[which(ind == s)] <- LambdaSt@x[which(ind == s)] <- 1
diagonal <- diag(LambdaS)
diag(LambdaS) <- diag(LambdaSt) <- 0
Ds <- LambdaS + LambdaSt
diag(Ds) <- diagonal
model$Wlist[[s]] <- tcrossprod(Z %*% Ds, Z)
model$eWelist[[s]] <- as.numeric(e %*% model$Wlist[[s]] %*% e)
# model$Wlist[[s]] <- model$Wlist[[s]]/norm(model$Wlist[[s]], type = "F")
}
## Write everything into a return list
model$X <- X
model$n <- n
model$theta <- theta
model$Z <- Z
model$Lambda <- Lambda
model$Lambdat <- Lambdat
model$V0inv <- V0inv
model$A <- A
if (analytic) {
model$B <- matrix(0, length(theta), length(theta))
} else {
stop("Numerical Hessian not calculated in nlme::lme objects!")
# model$B <- m@optinfo$derivs$Hessian
}
model$C <- matrix(0, length(theta), n)
model$y <- y
model$e <- e
model$tye <- as.numeric(crossprod(y, e))
model$isREML <- m$method == "REML"
return(model)
}
getModelComponents.merMod <-
function(m, analytic) {
# A function that calculates all components needed to calculate the bias
# correction as in Greven & Kneib (2010)
#
# Args:
# m = Object of class lmerMod. Obtained by lmer()
# analytic = FALSE if the numeric hessian of the (restricted) marginal log-
# likelihood from the lmer optimization procedure should be used.
# Otherwise (default) TRUE, i.e. use a analytical version that
# has to be computed.
#
# Returns:
# model = List of components needed to calculate the bias correction
#
model <- list()
model$df <- NULL
X <- getME(m, "X")
n <- nrow(X)
Z <- getME(m, "Z")
theta <- getME(m, "theta")
Lambda <- getME(m, "Lambda")
Lambdat <- getME(m, "Lambdat")
model$Wlist <- list()
model$eWelist <- list()
L <- getME(m, "L")
w <- weights(m)
if(any(w!=1)){
model$R <- diag(1/w)
Rinv <- diag(w)
D0inv <- solve(tcrossprod(Lambda))
V0inv <- Rinv - crossprod(Rinv,Z) %*% solve(D0inv + t(Z)%*%Rinv%*%Z) %*% crossprod(Z,Rinv)
}else{
I_v0inv <- Matrix(0, n, n, sparse = TRUE)
diag(I_v0inv) <- 1
V0inv <- I_v0inv - crossprod(solve(L, system = "L") %*%
solve(L, Lambdat, system = "P") %*% t(Z))
}
# P <- diag(rep(1, n)) - X %*% chol2inv(getME(m, "RX")) %*% crossprod(X, V0inv)
## pre calculate matrices for faster computation
# A <- crossprod(P, V0inv)
A <- V0inv - crossprod(crossprod(X %*% solve(getME(m, "RX")), V0inv))
y <- getME(m, "y")
e <- y - getME(m, "mu")
## prepare list of derivative matrices W_j
ind <- getME(m, "Lind")
len <- rep(0, length(Lambda@x))
for(s in 1:length(theta)) {
# model$Wlist <- lapply(theta, function(s){
LambdaS <- Lambda
LambdaSt <- Lambdat
LambdaS@x <- LambdaSt@x <- len
LambdaS@x[which(ind == s)] <- LambdaSt@x[which(ind == s)] <- 1
diagonal <- diag(LambdaS)
diag(LambdaS) <- diag(LambdaSt) <- 0
Ds <- LambdaS + LambdaSt
diag(Ds) <- diagonal
model$Wlist[[s]] <- tcrossprod(Z %*% Ds, Z)
model$eWelist[[s]] <- as.numeric(e %*% model$Wlist[[s]] %*% e)
# model$Wlist[[s]] <- model$Wlist[[s]]/norm(model$Wlist[[s]], type = "F")
}
## Write everything into a return list
model$X <- X
model$n <- n
model$theta <- theta
model$Z <- Z
model$Lambda <- Lambda
model$Lambdat <- Lambdat
model$V0inv <- V0inv
model$A <- A
if(analytic) {
model$B <- matrix(0, length(theta), length(theta))
} else {
model$B <- m@optinfo$derivs$Hessian
}
model$C <- matrix(0, length(theta), n)
model$y <- y
model$e <- e
model$tye <- as.numeric(crossprod(y, e))
model$isREML <- isREML(m)
return(model)
}
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