plot the iterations of optim vs the logliklihood values in r - r

Question

I have a function of Maximum likelihood estimation and I am using optim function. I would like to plot the iteration outputs vs the logliklihood values.
Here is an very similar example to my complex function:
y <- rnorm(1000,2,2)
myfunc <- function(x){
fn <- function(theta) { sum ( 0.5*(xvec - theta[1])^2/theta[2] + 0.5* log(theta[2]) ) }
optim(theta <- c(0,5), fn, hessian=TRUE,method = "L-BFGS-B",lower=c(0,0),control = list(trace=1))
}
The output is:
iter 10 value 12.001318
final value 12.001318
iter 10 is the iteration step.
value 12.001318 is the logliklihood value.
my function return 100 of them. I know that I need to store them first and then plot them. But how to do that in R?
any help please?

Answer 1

There are a couple of options. Option 1: add REPORT = 1 in the control list and the function value will be printed at each step. You would have to process this printed data somehow, maybe by using sink() and then removing the extra text. Option 2: run optim one iteration at a time and storing values. You can then easily store the log likelihood value and plot it. Partial code for both options is presented below.
# generating random values
set.seed(10)
y <- rnorm(1000,2,2)
#### option 1 ####
# intermediate results printed
myfunc <- function(xvec){
fn <- function(theta) { sum ( 0.5*(xvec - theta[1])^2/theta[2] + 0.5* log(theta[2]) ) }
optim(theta <- c(0,5), fn, hessian=TRUE,method = "L-BFGS-B",lower=c(0,0),control = list(trace=1,REPORT=1))
}
# running optimization with input y
myfunc(y)
# would need to copy values or otherwise post process to make plot
#### option 2 ####
# running optimization one iteration at a time
fn <- function(theta) { sum ( 0.5*(y - theta[1])^2/theta[2] + 0.5* log(theta[2]) ) }
# storing log likelihood values
loglvals <- fn(c(0,5))
# initializing variables
temp1par <- c(0,0)
# running the loop
for(i in 1:100){
temp1 <- optim(theta <- ifelse(i==1,1,0)*c(0,5)+ifelse(i==1,0,1)*temp1par, fn, hessian=TRUE,method = "L-BFGS-B",lower=c(0,0),control = list(trace=1,REPORT=1,maxit=1))
temp1par <- temp1$par
loglvals <- c(loglvals,temp1$value)
}
# plotting results
# trimming the length of loglvals because the function converged
# before the loop calling optim stopped
# simply using unique to specify when it the optim didn't return new values
plot(seq(0,length(unique(loglvals)),1)
,loglvals[seq(1,length(unique(loglvals))+1,1)]
,ylab='log likelihood'
,xlab='iteration')