Hi Josh
I have run the code and the structure of the output is what I wanted.
However, the code is giving an identical result for all runs.
I have attached the code I ran below as well as the output. I have just
changed number of runs to match with the size of the data.
a=read.table("D:/hope.txt",header=T)
attach(a)
a
#likilihood function
llik = function(x)
{
al_j=x[1]; au_j=x[2]; sigma_j=x[3]; b_j=x[4]
sum(na.rm=T,
ifelse(a$R_j< 0, -log(1/(2*pi*(sigma_j^2)))-
(1/(2*(sigma_j^2))*(a$R_j+al_j-b_j*a$R_m))^2,
ifelse(a$R_j>0 , -log(1/(2*pi*(sigma_j^2)))-
(1/(2*(sigma_j^2))*(a$R_j+au_j-b_j*a$R_m))^2,
-log(ifelse (( pnorm (au_j, mean=b_j *
a$R_m,
sd= sqrt(sigma_j^2))-
pnorm(al_j, mean=b_j * a$R_m, sd=sqrt (sigma_j^2)
)) > 0,
(pnorm (au_j,mean=b_j * a$R_m, sd=
sqrt(sigma_j^2))-
pnorm(al_j, mean=b_j * a$R_m, sd=
sqrt(sigma_j^2) )),
1)) ))
)
}
start.par = c(-0.01,0.01,0.1,1)
#looping now
runs=133/20+1 #total data points divided by number od days in each quater+1
out <- matrix(NA, nrow = runs, ncol = 4,
dimnames = list(paste("Quater:", 1:runs, sep = ''),
c("al_j", "au_j", "sigma_j", "b_j")))
for (i in 1:runs) {
a[seq(20 * (i - 1) +1, 20 * i), ]
out[i, ] <- optim(llik, par = start.par, method = "Nelder-Mead")[[1]]
}
out
#results I am getting
> out
al_j au_j sigma_j b_j
Quater:1 0.04001525 0.06006251 -7.171336e-25 1.049982
Quater:2 0.04001525 0.06006251 -7.171336e-25 1.049982
Quater:3 0.04001525 0.06006251 -7.171336e-25 1.049982
Quater:4 0.04001525 0.06006251 -7.171336e-25 1.049982
Quater:5 0.04001525 0.06006251 -7.171336e-25 1.049982
Quater:6 0.04001525 0.06006251 -7.171336e-25 1.049982
Quater:7 0.04001525 0.06006251 -7.171336e-25 1.049982
>
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