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