[R] Problem with Newton_Raphson

[Christopher Kelvin]

By your suggestion if i understand you, i have changed (p[1]) and (p[2]) and have also corrected the error sum(t), but if i run it, the parameters estimates are very large.
Can you please run it and help me out? The code is given below.


p1<-0.6;b<-2
n=20;rr=5000
U<-runif(n,0,1)
for (i in 1:rr){

x<-(-log(1-U^(1/p1))/b)

 meantrue<-gamma(1+(1/p1))*b
  meantrue
  d<-meantrue/0.30
  cen<- runif(n,min=0,max=d)
  s<-ifelse(x<=cen,1,0)
  q<-c(x,cen)
}
    z<-function(data, p){ 
    shape<-p[1]
    scale<-p[2]
    log1<-n*sum(s)*log(shape)+ n*sum(s)*log(scale)+(shape-1)*sum(s)*log(1-((exp(-(scale)*sum(x)))))
-(scale)*sum(x) + (shape)*log((exp(-(scale)*sum(x))))-
(shape)*sum(s)*log((exp(-(scale)*sum(x))))
  return(-log1)
  }
  start <- c(1,1)
  zz<-optim(start,fn=z,data=q,hessian=T)
  zz



Thank you
Chris Kelvin








----- Original Message -----
From: Berend Hasselman <bhh at xs4all.nl>
To: Christopher Kelvin <chris_kelvin2001 at yahoo.com>
Cc: "r-help at r-project.org" <r-help at r-project.org>
Sent: Thursday, September 20, 2012 8:52 PM
Subject: Re: [R] Problem with Newton_Raphson


On 20-09-2012, at 13:46, Christopher Kelvin wrote:

> Hello,
> I have being trying to estimate the parameters of the generalized exponential distribution. The random number generation for the GE distribution is x<-(-log(1-U^(1/p1))/b), where U stands for uniform dist. The data i have generated to estimate the parameters is right censored and the code is given below; The problem is that, the newton-Raphson approach isnt working and i do not know what is wrong. Can somebody suggest something or help in identifying what the problem might be. 
> 

Newton-Raphson? is not a method for optim.

> p1<-0.6;b<-2
> n=20;rr=5000
> U<-runif(n,0,1)
> for (i in 1:rr){
> 
> x<-(-log(1-U^(1/p1))/b)
> 
>  meantrue<-gamma(1+(1/p1))*b
>   meantrue
>   d<-meantrue/0.30
>   cen<- runif(n,min=0,max=d)
>   s<-ifelse(x<=cen,1,0)
>   q<-c(x,cen)
> 
>     z<-function(data, p){ 
>     shape<-p[1]
>     scale<-p[2]
>     log1<-n*sum(s)*log(p[1])+ n*sum(s)*log(p[2])+(p[1]-1)*sum(s)*log(1-((exp(-(p[2])*sum(x)))))
> -(p[2])*sum(t) + (p[1])*log((exp(-(p[2])*sum(x))))-
> (p[1])*sum(s)*log((exp(-(p[2])*sum(x))))
>   return(-log1)
>   }
> }
>   start <- c(1,1)
>   zz<-optim(start,fn=z,data=q,hessian=T)
>   zz
>   m1<-zz$par[2]
>   p<-zz$par[1] 
> 

Running your code given above gives an error message:

Error in sum(t) : invalid 'type' (closure) of argument

Where is object 't'?

Why are you defining function z within the rr loop? Only the last definition is given to optim.
Why use p[1] and p[2] explicitly in the calculation of log1 in the body of function z when you can use shape and scale defined in the lines before log1 <-.

Berend