[EMAIL PROTECTED] wrote:
>Hi,
>
>I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and
>\beta_1, this can be achieved by solving the following three equations:
>
>n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) -
>\sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0
>
>\alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1)
>\sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0
>
>\alpha \sum\limits_{i=1}^{n} ( i / \psihat(i) ) - (\alpha + 1)
>\sum\limits_{i=1}^{n} ( i / (x_i + \psihat(i)) ) = 0
>
>where \psihat=\beta_0 + \beta_1 * i. Now i want to get iterated values
>for \alpha, \beta_0 and \beta_1, so i used the following implementation
>
># first equation
>l1 <- function(beta0,beta1,alpha,x) {
> n<-length(x)
> s2<-length(x)
> for(i in 1:n) {
> s2[i]<-log(beta0+beta1*i)-log(x[i]+beta0+beta1*i)
> }
> s2<-sum(s2)
> return((n/alpha)+s2)
>}
>
># second equation
>l2 <- function(beta0,beta1,alpha,x) {
> n<-length(x)
> s1<-length(x)
> s2<-length(x)
> for(i in 1:n) {
> s1[i]<-1/(beta0+beta1*i)
> s2[i]<-1/(beta0+beta1*i+x[i])
> }
> s1<-sum(s1)
> s2<-sum(s2)
> return(alpha*s1-(alpha+1)*s2)
>}
>
>#third equation
>l3 <- function(beta0,beta1,alpha,x) {
> n<-length(x)
> s1<-length(x)
> s2<-length(x)
> for(i in 1:n) {
> s1[i]<-i/(beta0+beta1*i)
> s2[i]<-i/(x[i]+beta0+beta1*i)
> }
> s1<-sum(s1)
> s2<-sum(s2)
> return(alpha*s1-(alpha+1)*s2)
>}
>
># all equations in one
>gl <- function(beta0,beta1,alpha,x) {
> l1(beta0,beta1,alpha,x)^2 + l2(beta0,beta1,alpha,x)^2 +
>l3(beta0,beta1,alpha,x)^2
>}
>
>#iteration with optim
>optim(c(1,1,1),gl,x)
>
>i get always an error massage. Is optim anyway the 'right' method to get
>all three parameters iterated at the same time?
>
>best regards
>Andreas
>
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>
>
>
>
hi sundar,
your advice has helped very much, thanks a lot.
now i have another model where instead of i i2 is used, but i don't
now way i got so large estimates?
x<-c(10,8,14,17,15,22,19,27,35,40)
# 1.Gleichung
l1 <- function(beta0,beta1,alpha,x) {
n<-length(x)
s2<-length(x)
for(i in 1:n) {
s2[i]<-log(beta0+beta1*i2)-log(x[i]+beta0+beta1*i2)
}
s2<-sum(s2)
return((n/alpha)+s2)
}
# 2.Gleichung
l2 <- function(beta0,beta1,alpha,x) {
n<-length(x)
s1<-length(x)
s2<-length(x)
for(i in 1:n) {
s1[i]<-1/(beta0+beta1*i2)
s2[i]<-1/(beta0+beta1*i2+x[i])
}
s1<-sum(s1)
s2<-sum(s2)
return(alpha*s1-(alpha+1)*s2)
}
# 3.Gleichung
l3 <- function(beta0,beta1,alpha,x) {
n<-length(x)
s1<-length(x)
s2<-length(x)
for(i in 1:n) {
s1[i]<-(i2)/(beta0+beta1*i2)
s2[i]<-(i2)/(x[i]+beta0+beta1*i2)
}
s1<-sum(s1)
s2<-sum(s2)
return(alpha*s1-(alpha+1)*s2)
}
# Zusammenfügen aller Teile
gl <- function(beta,x) {
beta0<-beta[1]
beta1<-beta[2]
alpha<-beta[3]
v1<-l1(beta0,beta1,alpha,x)2
v2<-l2(beta0,beta1,alpha,x)2
v3<-l3(beta0,beta1,alpha,x)2
v1+v2+v3
}
# Nullstellensuche mit Nelder-Mead
optim(c(20000,6000,20000),gl,x=x,control=list(reltol=1e-12))
the values should be alpha=20485, beta0=19209 and beta1=6011
and another point is, what is a good method to find good starting values
for 'optim'. it seems, that i only get the desired values when the
starting values are in the same region. I used
control=list(reltol=1e-12), but it seems, that then it is also important
to have the starting values in the same region as the the desired values.
regards
andreas
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