Why exactly the same question? You were told what to do. I am new to R. I did what the previous poster said. I found it. Write your function in terms of vector operations. Avoid loops if you can. Sample input for R follows ------------------------- # par is the thing that has to be found # x are the observations f <- function(par,x) sum(2*(x-par)/(1+(x-par)^2)) # trial stuff x <- 1:10 # use uniroot to find a value for par such that f(par,x) == 0 # interval for par is obvious (lower=1 and upper=10) paropt <- uniroot(f,c(1,10),tol=1e-8,x) paropt <END OF SAMPLEINPUT> It works. Berend Hasselman
On 30 Nov 2007, at 17:59, stathelp wrote:
I have this maximum liklihood estimate problem
i need to find the roots of the following:
[sum (from i=1 to n) ] ((2(x[i]-parameter)/(1+(x[i]-parameter)^2))=0
given to me is the x vector which has length 100
how would I find the roots using R?
I have 2 thoughts...... 1 is using a grid search ... eg. brute
force, just
choosing a whole bunch of different values for my parameter ....
such as
parameter=seq(0,100,.1) .... and this is what I have so far,
john=rep(0,length(x))
for(i in 1:length(x)) {
john[i]=((x[i]-0)/(1+(x[i]-0)^2))
}
sum(john)
then
john=rep(0,length(x))
for(i in 1:length(x)) {
john[i]=((x[i]-.1)/(1+(x[i]-.1)^2))
}
sum(john)
then
john=rep(0,length(x))
for(i in 1:length(x)) {
john[i]=((x[i]-.2)/(1+(x[i]-.2)^2))
}
sum(john)
something like this ...
theta=seq(0,100,.1)
john=rep(0,length(x))
for(i in 1:length(x)) {
john[i]=((x[i]-theta[j])/(1+(x[i]-theta[j])^2))
}
sum(john)
but something is wrong with my code because its not working. Anyone
have any
ideas? (I am very new to R and statistical software in general)
The 2nd thought was to use the Newton Raphson Method, but, I dont
even know
where to start with that.
Any thoughts help.
Thanks
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