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 <b...@xs4all.nl>
To: Christopher Kelvin <chris_kelvin2...@yahoo.com>
Cc: "r-help@r-project.org" <r-help@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
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