Hello: Below is a toy logistic regression problem. When I wrote my own code, Newton-Raphson converged in three iterations using both the gradient and the Hessian and the starting values given below. But I can't get nlm() to work! I would much appreciate any help. > x [1] 10.2 7.7 5.1 3.8 2.6 > y [1] 9 8 3 2 1 > n [1] 10 9 6 8 10 derfs4=function(b,x,y,n) { b0 = b[1] b1 = b[2] c=b0+b1*x d=exp(c) p=d/(1+d) e=d/(1+d)^2 f = -sum(log(choose(n,y))-n*log(1+d)+y*c) attr(f,"gradient")=c(-sum(y-n*p),-sum(x*(y-n*p))) attr(f,"hessian")=matrix(c(sum(n*e),sum(n*x*e),sum(n*x*e),sum(n*x^2*e)),2,2) return(f) } > nlm(derfs4,c(-3.9,.64),hessian=T,print.level=2,x=x,y=y,n=n) Error in choose(n, y) : argument "n" is missing, with no default > I tried a variety of other ways too. When I got it to work it did not converge in 100 iterations ;rather like the fgh example given on the lme help page. Mervyn [[alternative HTML version deleted]]