Don't forget to add the max value of the likelihood to the quadratic fit. That will pull the quadratic fit to your lnL function. -----Original Message----- From: gov2001-l-bounces at lists.fas.harvard.edu [mailto:gov2001-l-bounces at lists.fas.harvard.edu] On Behalf Of Keith Schnakenberg Sent: Thursday, March 13, 2008 12:15 PM To: gov2001-l at lists.fas.harvard.edu Subject: [gov2001-l] quadratic approximation My quadratic approximation seems to have a maximum near my MLE, but is vertically way off. I'm not sure if this should matter or not, but it's not like that in the example so I think it means something has gone wrong. I was hoping somebody could take a look at my code for the quadratic approximation and give me some clue as to where I am going wrong. I have implemented the Fisher information as follows: fi <- function(par, x=dat$yearsinoffice){ lambda <- par[1] out <- (-1*(((-1*length(x))/mean(x)^2) + ((2*x + 2*length(x))/mean(x) ^3))) return(out) } I made vectors for plotting the log likelihood (ll) an quadratic approximation like this: a <- seq(from = 3, to = 10, by = .01) out <- c() for (i in 1:length(a)){ out[i] <- ll(a[i], x=dat$yearsinoffice) } out2 <- c() for (i in 1:length(a)){ out2[i]<- -1/2*fi(mle.num$par, x=dat$yearsinoffice)*(a[i]-mle.num$par)^2 } This does not seem to cause problems for the maximum value of my quadratic approximation, but it it makes it far enough off on the y axis that I can't get them on the same chart. I know the MLE should not be affected by shifting the thing up and down, but this makes me suspicious that I've implemented something wrong and my confidence intervals might be wrong as a result. _______________________________________________ gov2001-l mailing list gov2001-l at lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l