Nothing
glmm_final_smooth_noRE<-function(y,X,Phi,penal.vec,K,Delta_start,steps=1000,family,overdispersion,
phi,nue=1,print.iter.final=FALSE,flushit,eps.final=1e-5)
{
## Print stuff.
ia <- if(flushit) interactive() else FALSE
dim.smooth<-dim(Phi)[2]
N<-length(y)
lin<-ncol(as.matrix(X))
Eta<-cbind(X,Phi)%*%Delta_start
if(is.null(family$multivariate)){
D<-family$mu.eta(Eta)
Mu<-family$linkinv(Eta)
SigmaInv <- 1/family$variance(Mu)
}else{
Mu <- family$linkinv(Eta, K)
D <- family$deriv.mat(Eta, K)
SigmaInv <- family$SigmaInv(Mu, K)
}
if(print.iter.final)
# message()
{
cat(if(ia) "\r" else NULL)
cat("\nFinal Re-estimation Iteration 1")
if(.Platform$OS.type != "unix" & ia) flush.console()
}
Z_alles<-cbind(X,Phi)
P1<-c(rep(0,lin),penal.vec)
P1<-diag(P1)
Delta<-matrix(0,steps,(lin+dim.smooth))
Eta.ma<-matrix(0,steps+1,N)
Eta.ma[1,]<-Eta
l=1
opt<-steps
if(is.null(family$multivariate)){
D <- drop(D);SigmaInv <- drop(SigmaInv)
score_vec <- t(Z_alles)%*%((y-Mu)*D*SigmaInv)-P1%*%Delta[1,]
F_gross<-t(Z_alles)%*%(Z_alles*D*SigmaInv*D)+P1
}else{
score_vec<-RcppEigenProd1(Z_alles, D, SigmaInv, y, Mu)-P1%*%Delta[1,]
W_opt <- RcppEigenProd2(D, SigmaInv)
F_gross <- t(Z_alles)%*%(W_opt%*%Z_alles)+P1
}
InvFisher<-try(chol2inv(chol(F_gross)),silent=TRUE)
if(inherits(InvFisher, "try-error"))
InvFisher<-try(solve(F_gross),silent=TRUE)
if(inherits(InvFisher, "try-error"))
stop("Fisher matrix not invertible")
half.index<-0
solve.test<-FALSE
Delta_r<-InvFisher%*%score_vec
######### big while loop for testing if the update leads to Fisher matrix which can be inverted
while(!solve.test)
{
if(half.index>50)
half.index<-Inf
Delta[1,]<-Delta_start+nue*(0.5^half.index)*Delta_r
Eta<-Z_alles%*%Delta[1,]
if(is.null(family$multivariate)){
D<-family$mu.eta(Eta)
Mu<-family$linkinv(Eta)
SigmaInv <- 1/family$variance(Mu)
}else{
Mu <- family$linkinv(Eta, K)
D <- family$deriv.mat(Eta, K)
SigmaInv <- family$SigmaInv(Mu, K)
}
if(is.null(family$multivariate)){
D <- as.vector(D);SigmaInv <- as.vector(SigmaInv)
score_vec <- t(Z_alles)%*%((y-Mu)*D*SigmaInv)-P1%*%Delta[1,]
F_gross<-t(Z_alles)%*%(Z_alles*D*SigmaInv*D)+P1
}else{
score_vec<-RcppEigenProd1(Z_alles, D, SigmaInv, y, Mu)-P1%*%Delta[1,]
W_opt <- RcppEigenProd2(D, SigmaInv)
F_gross <- t(Z_alles)%*%(W_opt%*%Z_alles)+P1
}
InvFisher<-try(chol2inv(chol(F_gross)),silent=TRUE)
if(inherits(InvFisher, "try-error"))
InvFisher<-try(solve(F_gross),silent=TRUE)
if(inherits(InvFisher, "try-error"))
{
half.index<-half.index+1
}else{
solve.test<-TRUE
}
}
Eta.ma[2,]<-Eta
y_dach<-as.vector(family$linkinv(Eta))
Dev_neu<-sum(family$dev.resids(y,y_dach,wt=rep(1,N))^2)
###############################################################################################################################################
################################################################### Main Iteration ###################################################################
eps<-eps.final*sqrt(length(Delta_r))
for (l in 2:steps)
{
if(print.iter.final)
# message("Iteration ",l)
{
cat(if(ia) "\r" else if(l > 1) "\n" else NULL)
cat(paste("Final Re-estimation Iteration ",l))
if(.Platform$OS.type != "unix" & ia) flush.console()
}
half.index<-0
solve.test<-FALSE
Delta_r<-InvFisher%*%score_vec
######### big while loop for testing if the update leads to Fisher matrix which can be inverted
first.time<-FALSE
while(!solve.test)
{
if(half.index>50)
half.index<-Inf
Delta[l,]<-Delta[l-1,]+nue*(0.5^half.index)*Delta_r
Eta<-Z_alles%*%Delta[l,]
if(is.null(family$multivariate)){
D<-family$mu.eta(Eta)
Mu<-family$linkinv(Eta)
SigmaInv <- 1/family$variance(Mu)
}else{
Mu <- family$linkinv(Eta, K)
D <- family$deriv.mat(Eta, K)
SigmaInv <- family$SigmaInv(Mu, K)
}
if(is.null(family$multivariate)){
D <- as.vector(D);SigmaInv <- as.vector(SigmaInv)
score_vec <- t(Z_alles)%*%((y-Mu)*D*SigmaInv)-P1%*%Delta[l,]
F_gross<-t(Z_alles)%*%(Z_alles*D*SigmaInv*D)+P1
}else{
score_vec<-RcppEigenProd1(Z_alles, D, SigmaInv, y, Mu)-P1%*%Delta[l,]
W_opt <- RcppEigenProd2(D, SigmaInv)
F_gross <- t(Z_alles)%*%(W_opt%*%Z_alles)+P1
}
InvFisher<-try(chol2inv(chol(F_gross)),silent=TRUE)
if(inherits(InvFisher, "try-error"))
InvFisher<-try(solve(F_gross),silent=TRUE)
if(inherits(InvFisher, "try-error"))
{
half.index<-half.index+1
}else{
solve.test<-TRUE
}
}
Eta.ma[l+1,]<-Eta
finish<-(sqrt(sum((Eta.ma[l,]-Eta.ma[l+1,])^2))/sqrt(sum((Eta.ma[l,])^2))<eps)
finish2<-(sqrt(sum((Eta.ma[l-1,]-Eta.ma[l+1,])^2))/sqrt(sum((Eta.ma[l-1,])^2))<eps)
if(finish || finish2)
break
}
######## Final calculation
opt<-l
if(is.null(family$multivariate)){
W_opt <- D*SigmaInv*D
FinalHat<-(Z_alles*sqrt(W_opt))%*%InvFisher%*%t(Z_alles*sqrt(W_opt))
}else{
W_inv_t <- RcppEigenSpChol(W_opt)
FinalHat <- RcppEigenProd3(W_inv_t, Z_alles, InvFisher)
}
complexity<-sum(diag(FinalHat))
if(overdispersion)
phi<-(sum((y-Mu)^2/family$variance(Mu)))/(N-complexity)
Deltafinal<-Delta[l,]
Standard_errors<-InvFisher
#### compare Wood's Book on page 167
if(is.null(family$multivariate)){
EDF.matrix<-InvFisher%*%(t(Z_alles)%*%(Z_alles*D*SigmaInv*D))
}else{
EDF.matrix<-InvFisher%*%(t(Z_alles)%*%(D%*%(SigmaInv%*%(t(D)%*%Z_alles))))
}
ret.obj=list()
ret.obj$opt<-opt
ret.obj$Delta<-Deltafinal
ret.obj$Standard_errors<-Standard_errors
ret.obj$phi<-phi
ret.obj$EDF.matrix<-EDF.matrix
ret.obj$complexity<-complexity
return(ret.obj)
}
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