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##' @export
updatelvm <- function(x,mean=TRUE,...) {
index(x) <- reindex(x,mean=mean,...)
x$parpos <- parpos(x,mean=mean,...)
return(x)
}
##' @export
"index" <- function(x,...) UseMethod("index")
##' @export
"index<-" <- function(x,...,value) UseMethod("index<-")
##' @export
"index.lvm" <- function(x,...) { x$index }
##' @export
"index.lvmfit" <- function(x,...) { index(Model(x)) }
##' @export
"index<-.lvm" <- function(x,...,value) { x$index <- value; return(x) }
##' @export
"index<-.lvmfit" <- function(x,...,value) { Model(x)$index <- value; return(x) }
### A ## Matrix with fixed parameters and ones where parameters are free
### J ## Manifest variable selection matrix
### M0 ## Index of free regression parameters
### M1 ## Index of free and _unique_ regression parameters
### P ## Matrix with fixed variance parameters and ones where parameters are free
### P0 ## Index of free variance parameters
### P1 ## Index of free and _unique_ regression parameters
### npar.var ## Number of covariance parameters
##' @export
`reindex` <-
function(x, sparse=FALSE,standard=TRUE,zeroones=FALSE,deriv=FALSE,mean=TRUE) { ## Extract indices of parameters from model
x$parpos <- NULL
M <- x$M
eta <- latent(x) ## Latent variables/Factors
m <- length(eta)
obs <- manifest(x) ## Manifest/Observed variables
endo <- endogenous(x)
exo <- exogenous(x) ##,index=FALSE)
allvars <- vars(x)
eta.idx <- na.omit(match(eta,allvars))
obs.idx <- na.omit(match(obs,allvars))
exo.idx <- na.omit(match(exo,allvars))
exo.obsidx <- na.omit(match(exo,obs))
endo.obsidx <- na.omit(match(endo,obs))
fix.idx <- !is.na(x$fix) ## Index of fixed parameters
covfix.idx <- !is.na(x$covfix) ## Index of fixed covariance parameters
constrain.par <- NULL
if (length(constrain(x))>0) constrain.par <- names(constrain(x))
M0 <- M; M0[fix.idx] <- 0 ## Matrix of indicators of free regression-parameters (removing fixed parameters)
M1 <- M0; ## Matrix of indiciator of free _unique_ regression parameters (removing fixed _and_ duplicate parameters)
parname <- unique(x$par[!is.na(x$par)])
for (p in parname) {
ii <- which(x$par==p)
if (length(ii)>1)
M1[ii[-1]] <- 0
if (p %in% constrain.par)
M0[ii] <- M1[ii] <- 0
}
npar.reg <- sum(M1) ## Number of free regression parameters
P <- x$cov;
P0 <- P; P0[covfix.idx] <- 0 ## Matrix of indicators of free covariance-parameters (removing fixed parameters)
if (length(exo.idx)>0)
P0[exo.idx,exo.idx] <- 0 ## 6/1-2011
P1 <- P0 ## Matrix of indiciator of free _unique_ variance parameters (removing fixed _and_ duplicate parameters)
covparname <- unique(x$covpar[!is.na(x$covpar)])
for (p in covparname) {
ii <- which(x$covpar==p)
if (length(ii)>1)
P1[ii[-1]] <- 0
if (p%in%c(parname,constrain.par))
P0[ii] <- P1[ii] <- 0
}
npar.var <- sum(c(diag(P1),P1[lower.tri(P1)]))
A <- M
A[fix.idx] <- x$fix[fix.idx] ## ... with fixed parameters in plac
P[covfix.idx] <- x$covfix[covfix.idx] ## ... with fixed parameters in plac
px <- Jy <- J <- diag(nrow=length(vars(x)))
if (m>0) {
J[eta.idx,eta.idx] <- 0; J <- J[-eta.idx,,drop=FALSE]
} ## Selection matrix (selecting observed variables)
{
## Selection matrix (selection endogenous variables)
if (length(c(eta.idx,exo.idx))>0) {
Jy[c(eta.idx,exo.idx),c(eta.idx,exo.idx)] <- 0; Jy <- Jy[-c(eta.idx,exo.idx),,drop=FALSE]
}
## Cancelation matrix (cancels rows with exogenous variables)
px[exo.idx,exo.idx] <- 0
}
## Creating indicitor of free mean-parameters
fixed <- sapply(x$mean, function(y) is.numeric(y) & !is.na(y))
named <- sapply(x$mean, function(y) is.character(y) & !is.na(y))
mparname <- NULL
if (length(named)>0)
mparname <- unlist(unique(x$mean[named]))
v0 <- rep(1,length(x$mean)) ## Vector of indicators of free mean-parameters
v0[exo.idx] <- 0
if (length(fixed)>0) v0[fixed] <- 0;
v1 <- v0
for (p in mparname) {
idx <- which(x$mean==p)
if (length(idx)>1) {
v1[idx[-1]] <- 0
}
if (p%in%c(parname,covparname,constrain.par))
v0[idx] <- v1[idx] <- 0
} ## duplicate parameters
###
### Extra parameters
###
efixed <- sapply(x$exfix, function(y) is.numeric(y) & !is.na(y))
enamed <- sapply(x$exfix, function(y) is.character(y) & !is.na(y))
if(length(enamed)>0){
eparname <- unlist(unique(x$exfix[enamed]))
} else{
eparname <- NULL
}
## Extra parameters
e0 <- rep(1,length(x$expar)) ## Indicators of free extra par.
if (length(efixed)>0)
e0[efixed] <- 0
e1 <- e0
for (p in eparname) {
idx <- which(x$exfix==p)
if (length(idx)>1) {
e1[idx[-1]] <- 0
}
if (p%in%c(parname,covparname,constrain.par,mparname))
e0[idx] <- e1[idx] <- 0
} ## duplicate parameters
## Return:
## Adjacency-matrix (M)
## Matrix of regression-parameters (0,1) _with_ fixed parameters (A)
## Matrix of variance-parameters (indicators 0,1) (P)
## Manifest selection matrix (J),
## Position of variables matrix (Apos),
## Position of covariance variables matrix (Ppos),
## Position/Indicator matrix of free regression parameters (M0)
res <- list(vars=allvars, manifest=obs, exogenous=exo, latent=eta,
endogenous=endo,
exo.idx=exo.idx, eta.idx=eta.idx,
exo.obsidx=exo.obsidx, endo.obsidx=endo.obsidx,
obs.idx=obs.idx,
endo.idx=setdiff(obs.idx,exo.idx))
if (standard) {
res <- c(res, list(M=M, A=A, P=P,
P0=P0, P1=P1,
M0=M0, M1=M1,
v0=v0, v1=v1,
e0=e0, e1=e1,
npar=(npar.reg+npar.var),
npar.reg=npar.reg,
npar.var=npar.var,
npar.mean=sum(v1),
npar.ex=sum(e1),
constrain.par=constrain.par))
which.diag <- NULL
if (length(P1)>0)
which.diag <- which(diag(P1==1))
res <- c(res, list(parname.all=parname, parname=setdiff(parname,constrain.par),
which.diag=which.diag,
covparname.all=covparname,
covparname=setdiff(covparname,constrain.par),
meanfixed=fixed, meannamed=named,
mparname.all=mparname,
mparname=setdiff(mparname,constrain.par),
eparname.all=eparname,
eparname=setdiff(eparname,constrain.par),
J=J, Jy=Jy, px=px, sparse=sparse))
parname.all.reg.idx <- parname.all.reg.tidx <-
parname.reg.tidx <- parname.reg.idx <- c()
for (p in res$parname.all) {
ipos <- which((x$par==p))
tipos <- which(t(x$par==p))
if (p%in%res$parname) {
parname.reg.idx <- c(parname.reg.idx, list(ipos))
parname.reg.tidx <- c(parname.reg.tidx, list(tipos))
}
parname.all.reg.idx <- c(parname.all.reg.idx, list(ipos))
parname.all.reg.tidx <- c(parname.all.reg.tidx, list(tipos))
};
if (length(parname.reg.idx)>0) {
names(parname.reg.idx) <- names(parname.reg.tidx) <- res$parname
}
if (length(parname.all.reg.idx)>0) {
names(parname.all.reg.idx) <- names(parname.all.reg.tidx) <- res$parname.all
}
covparname.all.idx <- covparname.idx <- c()
for (p in res$covparname.all) {
ipos <- which(x$covpar==p)
if (p%in%res$covparname)
covparname.idx <- c(covparname.idx, list(ipos))
covparname.all.idx <- c(covparname.all.idx, list(ipos))
};
if (length(covparname.idx)>0)
names(covparname.idx) <- res$covparname
if (length(covparname.all.idx)>0)
names(covparname.all.idx) <- res$covparname.all
mparname.all.idx <- mparname.idx <- c()
for (p in res$mparname.all) {
ipos <- which(x$mean==p)
if (p%in%mparname)
mparname.idx <- c(mparname.idx, list(ipos))
mparname.all.idx <- c(mparname.all.idx, list(ipos))
};
if (length(mparname.idx)>0)
names(mparname.idx) <- res$mparname
if (length(mparname.all.idx)>0)
names(mparname.all.idx) <- res$mparname.all
eparname.all.idx <- eparname.idx <- c()
for (p in res$eparname.all) {
ipos <- which(x$exfix==p)
if (p%in%eparname)
eparname.idx <- c(eparname.idx, list(ipos))
eparname.all.idx <- c(eparname.all.idx, list(ipos))
};
if (length(eparname.idx)>0)
names(eparname.idx) <- res$eparname
if (length(eparname.all.idx)>0)
names(eparname.all.idx) <- res$eparname.all
res <- c(res, list(mparname.idx=mparname.idx,
covparname.idx=covparname.idx,
parname.reg.idx=parname.reg.idx,
parname.reg.tidx=parname.reg.tidx,
mparname.all.idx=mparname.all.idx,
eparname.all.idx=eparname.all.idx,
covparname.all.idx=covparname.all.idx,
parname.all.reg.idx=parname.all.reg.idx,
parname.all.reg.tidx=parname.all.reg.tidx
))
} else {
res <- index(x)
}
if (zeroones) {
if (sparse) {
if (!requireNamespace("Matrix",quietly=TRUE)) stop("package Matrix not available")
Ik <- Matrix::Diagonal(length(obs))
Im <- Matrix::Diagonal(ncol(A))
Kkk <- NULL
J <- as(J, "sparseMatrix")
Jy <- as(Jy, "sparseMatrix")
px <- as(px, "sparseMatrix")
} else {
Ik <- diag(nrow=length(obs))
Im <- diag(nrow=ncol(A))
}
Kkk <- NULL
res[c("Ik","Im","Kkk")] <- NULL
res <- c(res, list(Ik=Ik, Im=Im, Kkk=Kkk))
}
if (deriv && length(P)>0) {
if (res$npar.mean>0 & mean)
D <- deriv.lvm(x,meanpar=rep(1,res$npar.mean),zeroones=TRUE)
else
D <- deriv.lvm(x,meanpar=NULL,zeroones=TRUE)
res[c("dA","dP","dv")] <- NULL
res <- c(res, list(dA=D$dA, dP=D$dP, dv=D$dv))
}
if (length(P)>0)
res <- c(res,mat.lvm(x,res))
return(res)
}
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