Hmmm... So you want to fit a nonlinear model with 24 parameters??!!
I would bet that you'll need to sacrifice some chickens to the
nonlinear optimization gods to get something meaningful from that
exercise.
:(
-- Bert
On Tue, May 15, 2012 at 5:26 PM, infinitehorizon
<barisvar...@hotmail.com> wrote:
> Hello John,
>
> Thanks for your response. Can you explain why it is better to use NM or nmk
> instead of optim? By the way, my original model consists 24 parameters. In
> this case is it still better to use NM or nmk?
>
> Best,
>
> Marc
>
>
>
> John C Nash wrote
>>
>> Your function will not evaluate as coded. i.e., llfn(start.par) doesn't
>> "work", as there
>> are unequal numbers of arguments. Also, while R allows you to use
>> variables that are not
>> explicitly defined for a function, I've almost always got into trouble if
>> I don't pass
>> them VERY carefully.
>>
>> Finally, as author of the code used to put CG in optim, I'll advise
>> against its use. One
>> of my least successful pieces of code. Rcgmin is better, but you really do
>> need analytic
>> derivatives to make it sing. For 5 parameters, use NM, or better the nmk
>> from dfoptim package.
>>
>> Best, JN
>>
>>
>> On 05/15/2012 06:00 AM, r-help-request@ wrote:
>>> Message: 13
>>> Date: Mon, 14 May 2012 04:21:57 -0700 (PDT)
>>> From: infinitehorizon <barisvardar@>
>>> To: r-help@
>>> Subject: Re: [R] Discrete choice model maximum likelihood estimation
>>> Message-ID: <1336994517063-4629910.post@.nabble>
>>> Content-Type: text/plain; charset=us-ascii
>>>
>>> Hello again,
>>>
>>> I changed the name to tt.
>>> and for a and tt actually I was getting them from data, I didn't put them
>>> here in the question. Now I restructured my code and below I copy the
>>> full
>>> code, I tried many things but still getting the same error, I don't
>>> understand where is the mistake.
>>>
>>> I also defined one more variable to increase comprehension of the
>>> problem.
>>> Instead of data, I define three representative vectors in the beginning.
>>> If
>>> you run this code, you will see the error message.
>>>
>>> # Variables, a: discrete choice variable-dependent, x and tt are
>>> independent
>>> variables, tt is binary
>>> a <- c(1,1,2,3,2,3,1,2,2,3,1,1)
>>> x <- c(23,26,12,27,10,30,13,20,23,44,17,15)
>>> tt <- c(1,0,0,1,1,1,0,0,1,0,1,1)
>>>
>>> # First, just to see, the linear model
>>>
>>> LM <-lm(a ~ x+tt)
>>> coefLM <- coefficients(LM)
>>> summary(LM)
>>>
>>> # Probabilities for discrete choices for a=3, a=2 and a=1 respectively
>>> P3 <- function(bx,b3,b,tt) {
>>> P <- exp(bx*x+b3+b*(tt==1))/(1-exp(bx*x+b3+b*(tt==1)))
>>> return(P)
>>> }
>>> P2 <- function(bx,b2,b,tt) {
>>> P <- exp(bx*x+b2+b*(tt==1))/(1-exp(bx*x+b2+b*(tt==1)))
>>> return(P)
>>> }
>>> P1 <- function(bx,b1,b,tt) {
>>> P <- exp(bx*x+b1+b*(tt==1))/(1-exp(bx*x+b1+b*(tt==1)))
>>> return(P)
>>> }
>>>
>>> # Likelihood functions for discrete choices for a=3, a=2 and a=1
>>> respectively
>>>
>>> L3 <- function(bx,b1,b2,b3,b,tt) {
>>> P11 <- P1(bx,b1,b,tt)
>>> P22 <- P2(bx,b2,b,tt)
>>> P33 <- P3(bx,b3,b,tt)
>>>
>>> L3l <- P11*P22*P33
>>> return(L3l)
>>> }
>>>
>>> L2 <- function(bx,b1,b2,b,tt) {
>>> P11 <- P1(bx,b1,b,tt)
>>> P22 <- P2(bx,b2,b,tt)
>>>
>>> L2l <- P11*P22
>>> return(L2l)
>>> }
>>>
>>> L1 <- function(bx,b1,b,tt) {
>>> P11 <- P1(bx,b1,b,tt)
>>>
>>> L1l <- P11
>>> return(L1l)
>>> }
>>>
>>> # Log-likelihood function
>>>
>>> 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
>>> }
>>> start.par <- c(0,0,0,0,0)
>>> est <- optim(param=start.par,llfn,x=x, a=a, tt=tt, method =
>>> c("CG"),control=list(trace=2,maxit=2000), hessian=TRUE)
>>>
>>
>> ______________________________________________
>> R-help@ 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.
>>
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/Discrete-choice-model-maximum-likelihood-estimation-tp4629877p4630199.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.
--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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