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...the prediction interval around the estimated value for this point t_next. I've found the formulae for the ponctual estimation of t_next but I cannot succeed in finding the formula to compute the prediction interval based on BMA sets of models, which requires the adequate variance. The paper of Hoeting et al. on BMA gives the posterior variance of a parameter but what I want is the posterior variance of a predicted Y and not the posterior variance of a beta, since the goal here is not to build an explanatory model but an accurately predictive model. Is there a way to get around it with the fu...

...ose to the actual maximum to get there. Surface plots might help, setting some variables to a constant. I know in 4D this will be tough. Here is a useful reference that helped me recently with a similar maximization problem: "Computational Statistics" by Geof H. Givens and Jennifer A. Hoeting They have R-code examples here: http://www.stat.colostate.edu/computationalstatistics/ Good luck! Joe Liddle University of Alaska Southeast

Dear R helpers, I need to estimate a probit model with box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not