This function takes the output of `optim`, `newton_raphson`, or `sim_anneal` and turns it into an `mle_numerical` (subclass of `mle`) object.

Usage

mle_numerical(sol, options = list(), superclasses = NULL)

Arguments

sol: the output of `optim` or `newton_raphson`
options: list, options for things like sigma and FIM
superclasses: list, superclasses to add to the `mle_numerical` object

Value

An object of class mle_numerical (subclass of mle).

Examples

# Fit exponential distribution using optim
set.seed(123)
x <- rexp(100, rate = 2)

# Log-likelihood for exponential distribution
loglik <- function(rate) {
  if (rate <= 0) return(-Inf)
  sum(dexp(x, rate = rate, log = TRUE))
}

# Optimize (maximize by setting fnscale = -1)
result <- optim(
  par = 1,
  fn = loglik,
  method = "Brent",
  lower = 0.01, upper = 10,
  hessian = TRUE,
  control = list(fnscale = -1)
)

# Wrap in mle_numerical
fit <- mle_numerical(result, options = list(nobs = length(x)))
params(fit)
#> [1] 1.91256
se(fit)
#> [1] 0.191256