search for: finalhessian

...uilt maximum likelihood functions, for the different methods (NR, BFGS, etc). I have figured out how to use all of them except the maxBHHH function. This one is different from the others as it requires an observation level gradient. I am using the following syntax: maxBHHH(logLik,grad=nuGradient,finalHessian="BHHH",start=prm,iterlim=2) where logLik is the likelihood function and returns a vector of observation level likelihoods and nuGradient is a function that returns a matrix with each row corresponding to a single observation and the columns corresponding to the gradient values for each p...

Now I've found another way to make it work. All I need is to pick up the names in the column (x.1.age...). > v<-pr(goprobit.p); v Maximum-Likelihood Estimates weighted = FALSE iterations = 5 logLik = -14160.75 finalHessian = TRUE Covariance matrix is Robust Number of parameters = 66 Sample size = 17922 est se t p g sig x.1.age 0.0341 0.0138 2.4766 0.0133 -3.8835e-04 ** x.1.sleep -0.1108 0.0059 -18.6277 0.0000 -4.4572e-04 *** x.1.primary -0.0694 0.0289 -2.4002 0.0164 -9.9638e-06 ** x.1.middle -0.2909 0.0356 -8.165...