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...= p3[1]} prob_fn[t5] = c(prob_ok) } prob_fn[prob_fn==0] = 0.0000000000001 ### LL ll_calc = -(sum(Y*log(prob_fn))) return(ll_calc) } res = mle2(Model, start=list(a=1, b=1, c=1, d=0.15, e=0.15, f=0.15, g=0.9, h=0.9, i=0.9, j=0.1, k=0.1, l=0.1), method = "Nelder- Mead") res Best regards, LFRC -- View this message in context: http://www.nabble.com/MLE-Constraints-tp19994553p19994553.html Sent from the R help mailing list archive at Nabble.com.

Dear, I'm starting on R language. I would like some help to implement a MLE function. I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res =