
I have a model with 5 parameters that I am optimising where the (best)
value of the objective function is negative. I would like to use the
Hessian matrix (from genoud and/or optim functions)  to construct  the
covariance and correlation matrices.

  This is the code that I am using:

  est <- out$par                  # Parameter estimates 
  H <- out$hessian             # Hessian 
  V <- solve(H)                   # Covariance matrix
  s <- sqrt(abs(diag(V)))    # Vector of standard deviations 
  cor <- V/(s%o%s)            # Correlation coefficient matrix 
  ci <- est+qnorm(0.975)*s%o%c(-1,1) # 95% CI's

However I am getting values that are greater than 1 (1.05, 2.34, etc)
for the correlation matrix. Might this be due to the fact that the
out$val is negative?

Thank you
Amanda

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