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Dear all, I'd like to find the mle esttimates using the mle function mle(negloglik, start = list(), fixed=list(), method="..."). I am using the L-BGFS-B method and I don't supply the gradient function. Is there a way to print the gradients found at the solution value? I am using R-1.9.1 on Windows and on Unix. Thank you in advance, Victoria Landsman. [[alternative HTML version deleted]]

...in ftp://ece.nwu.edu/pub/lbfgs/lbfgs_bcm/ but that directory seems not to exist. (Actually, the comment does not give a URL - it just says to look in that directory on that FTP server.) Paper [1] does exist in ftp://ece.nwu.edu/pub/nocedal/papers/sisc95.ps Nocedal's bibliography page for L-BGFS-B is http://www.ece.nwu.edu/~nocedal/lbfgsb.html which points to .ps.gz forms of both papers. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or &...

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

I have to estimate the following model for several group of observations : y(1-y) = p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y) with constraints : p[1]+p[3] >= 1 p[1]+p[2]+p[3]+1 >= 0 p[3] >= 0 I use the following code : func <- sum((y(1-y) - p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y))^2) estim <- optim( c(1,0,0),func, method="L-BFGS-B" , lower=c(1-p[3], -p[1]-p[3]-1,