[R] Discrete choice model maximum likelihood estimation

[infinitehorizon]

Of course, that was the trick! It works now.  Thank you very much Rui, I am
very grateful.
I hope this thread will help others as well.

Best,



Rui Barradas wrote
> 
> Once again, sorry.
> I had a different llfn in my R session and it messed with yours.
> 
> llfn <- function(param, a, tt) {
> 
> 
> llfn <- sum((a==1)*lL1+(a==2)*lL2+(a==3)*lL3)  # sum of logs, it's a
> log-likelihood.
> return(-llfn)
> }
> 
> Rui Barradas
> 
> infinitehorizon wrote
>> 
>> Hello again,
>> 
>> You are absolutely right about probabilities.. Thanks for reminding me
>> about that.
>> 
>> I did exactly how you said but in the end I receive the error :
>> "objective function in optim evaluates to length 12 not 1".
>> I checked how llfn give a vector instead of scalar,  but couldn't figure
>> it out.
>> 
>> Can you please tell me how did you obtain those estimates?
>> Thanks again,
>> 
>> Best,
>> 
>> Marc
>> 
>> 
>> Rui Barradas wrote
>>> 
>>> Hello, again.
>>> 
>>> Bug report:
>>> 1. Your densities can return negative values, 1 - exp(...) < 0.
>>>     Shouldn't those be 1 PLUS exp()?
>>> 
>>> P3 <- function(bx,b3,b,tt) {
>>> 	P <- exp(bx*x+b3+b*(tt == 1))/(1+exp(bx*x+b3+b*(tt == 1)))
>>> 	return(P)
>>> }
>>> 
>>> And the same for P2 and P1?
>>> 
>>> 2. Include 'a' and 'tt' as llfn parameters and call like the following.
>>> 
>>> llfn <- function(param, a, tt) {
>>> 
>>>            [... etc ...]
>>>            return(-llfn)
>>> }
>>> 
>>> start.par <- rep(0, 5)
>>> est <- optim(start.par, llfn, gr=NULL, a=a, tt=tt)
>>> est
>>> $par
>>> [1]  4.1776294 -0.9952026 -0.7667640 -0.1933693  0.7325221
>>> 
>>> $value
>>> [1] 0
>>> 
>>> $counts
>>> function gradient 
>>>       44       NA 
>>> 
>>> $convergence
>>> [1] 0
>>> 
>>> $message
>>> NULL
>>> 
>>> 
>>> Note the optimum value of zero, est$value == 0
>>> 
>>> Rui Barradas
>>> 
>>> infinitehorizon wrote
>>>> 
>>>> By the way, in my last post I forgot to return negative of llfn, hence
>>>> the llfn will be as follows:
>>>> 
>>>> llfn <- function(param) { 
>>>> 
>>>> bx <- param[1] 
>>>> b1 <- param[2] 
>>>> b2 <- param[3] 
>>>> b3 <- param[4] 
>>>> b <- param[5] 
>>>> 
>>>> lL1 <- log(L1(bx,b1,b2,b,tt)) 
>>>> lL2 <- log(L2(bx,b1,b2,b3,b,tt)) 
>>>> lL3 <- log(L3(bx,b1,b2,b3,b,tt)) 
>>>> 
>>>> llfn <- (a==1)*lL1+(a==2)*lL2+(a==3)*lL3 
>>>> return(-llfn)
>>>> } 
>>>> 
>>>> However, it does not fix the problem, I still receive the same error..
>>>> 
>>> 
>> 
> 


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