Dears, I'm trying to find the parameters (a,b, ... l) that optimize the function (Model) described below.
1) How can I set some constraints with MLE2 function? I want to set p1>0, p2>0, p3>0, p1>p3. 2) The code is giving the following warning. Warning: optimization did not converge (code 1) How can I solve this problem? Can someone help me? M <- 14 Y = c(0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1) x1 = c(0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.25, 0.25, 0.25, 0.25) x2 = c(-1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1) x3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) states = c(1, 1, 2, 3, 1, 2, 3, 1, 1, 2, 2, 3, 1, 1) prob_fn = rep(0,M) Model=function(a, b, c, d, e, f, g, h, i, j, k, l) { p1 = exp(-(a g*x1 d*x2 j*x3)) p2 = exp(-(b h*x1 e*x2 k*x3)) p3 = exp(-(c i*x1 f*x2 l*x3)) ### Set P t5 = 0 while(t5<M) { t5 = t5 1 if(states[t5]==1) {prob_ok = p1[1]} if(states[t5]==2) {prob_ok = p2[1]} if(states[t5]==3) {prob_ok = 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. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.