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# last modified 2011-08-10 by J. Fox
optimizerNlm <- function(start, objective=objectiveML,
gradient=TRUE, maxiter, debug, par.size, model.description, warn, ...){
with(model.description, {
obj <- objective(gradient=gradient)$objective
typsize <- if (par.size == 'startvalues') abs(start) else rep(1, t)
if (!warn) save.warn <- options(warn=-1)
res <- nlm(obj, start, iterlim=maxiter, print.level=if(debug) 2 else 0,
typsize=typsize, hessian=TRUE, model.description, ...)
if (!warn) options(save.warn)
result <- list()
result$convergence <- res$code <= 2
result$iterations <- res$iterations
par <- res$estimate
names(par) <- param.names
result$par <- par
if (!result$convergence)
warning(paste('Optimization may not have converged; nlm return code = ',
res$code, '. Consult ?nlm.\n', sep=""))
vcov <- matrix(NA, t, t)
qr.hess <- try(qr(res$hessian), silent=TRUE)
if (class(qr.hess) == "try-error"){
warning("Could not compute QR decomposition of Hessian.\nOptimization probably did not converge.\n")
}
else if (qr.hess$rank < t){
warning(' singular Hessian: model is probably underidentified.\n')
which.aliased <- qr.hess$pivot[-(1:qr.hess$rank)]
result$aliased <- param.names[which.aliased]
}
else {
vcov <- (2/(N - (!raw))) * solve(res$hessian)
if (any(diag(vcov) < 0)) {
result$aliased <- param.names[diag(vcov) < 0]
warning("Negative parameter variances.\nModel may be underidentified.\n")
}
}
colnames(vcov) <- rownames(vcov) <- param.names
result$vcov <- vcov
result$criterion <- res$minimum # c(result$obj) - n - log(det(S))
obj <- obj(par, model.description)
C <- attr(obj, "C")
rownames(C) <- colnames(C) <- var.names[observed]
result$C <- C
A <- attr(obj, "A")
rownames(A) <- colnames(A) <- var.names
result$A <- A
P <- attr(obj, "P")
rownames(P) <- colnames(P) <- var.names
result$P <- P
class(result) <- "semResult"
result
}
)
}
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