Problem with Newton_Raphson

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

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