Dear All, I wanted to post some more details about the query I sent to s-news last week. I have a vector with a constraint. The constraint is that the sum of the vector must add up to 1 - but not necessarily positive, i.e. x[n] <- 1 -(x[1] + ...+x[n-1]) I perform the optimisation on the vector x such that x <- c(x, 1-sum(x)) In other words, fn <- function(x){ x <- c(x, 1 - sum(x)) # other calculations here } then feed this into nlminb() out <- nlminb(x, fn) out.x <- out$parameters out.x <- c(out.x, 1 - sum(out.x)) out.x I would like to calculate standard errors for each of the components of x. Is this possible by outputing the Hessian matrix? Furthermore, how would I calculate this for the last component (if this is indeed possible) which has the restriction (i.e. 1-sum(out.x))? Any help would be much appreciated. Regards, John [[alternative HTML version deleted]]
Dear All, I wanted to post some more details about the query I sent to s-news last week. I have a vector with a constraint. The constraint is that the sum of the vector must add up to 1 - but not necessarily positive, i.e. x[n] <- 1 -(x[1] + ...+x[n-1]) I perform the optimisation on the vector x such that x <- c(x, 1-sum(x)) In other words, fn <- function(x){ x <- c(x, 1 - sum(x)) # other calculations here } then feed this into nlminb() out <- nlminb(x, fn) out.x <- out$parameters out.x <- c(out.x, 1 - sum(out.x)) out.x I would like to calculate standard errors for each of the components of x. Is this possible by outputing the Hessian matrix? Furthermore, how would I calculate this for the last component (if this is indeed possible) which has the restriction (i.e. 1-sum(out.x))? Any help would be much appreciated. Regards, John [[alternative HTML version deleted]]