search for: tmph

...ncol = 3) for(i in 1:3){ v1 = X%*%Beta[(i-1)*2+1:2] v2 = Y%*%Beta[(i-1)*2+1:2] p1 = exp(v1)/(exp(v1)+exp(v2)) p2 = exp(v2)/(exp(v1)+exp(v2)) probs[,i] = ifelse (D==0,log(p1),log(p2)) } return (probs) } #H [individuals][class] E_step = function(alpha,Beta){#calc posterior of H tmpH = matrix(,nrow = 1000,ncol =3) lprobs = logProbInd(Beta) for(i in 1:3){#classes tmpH[,i] = alpha[i]*exp(lprobs[,i]) } H = tmpH /apply(tmpH,1,sum) return( H) } M_step = function(H,Beta){ #first part use direct estimation aita = apply(H,2,sum)/1000 opt.c = optim(Beta,Q2,H=H,method="...