Default MLE solver for positive parameters

Multi-start optimization via compositional.mle: L-BFGS-B with positivity constraints as the primary solver, Nelder-Mead on the log-parameter scale as fallback. Runs from n_starts random perturbations of par0 and returns the best result.

Usage

solve_mle(neg_ll, par0, n_par, n_starts = 5L, nobs = NULL)

Arguments

neg_ll

Function of par only: guarded negative log-likelihood (returns .Machine$double.xmax / 2 on failure).

par0

Numeric vector of initial parameter values.

n_par

Integer number of parameters.

n_starts

Integer number of random restarts (default 5).

nobs

Integer number of observations.

Value

An mle_numerical result object (from algebraic.mle) with coef(), vcov(), logLik(), etc. Returns NULL if all starts fail.

Examples

# Fit a simple exponential rate from positive data
x <- rexp(100, rate = 0.5)
neg_ll <- function(rate) -sum(dexp(x, rate, log = TRUE))
fit <- solve_mle(neg_ll, par0 = 1, n_par = 1, nobs = length(x))
#> Warning: NaNs produced
#> Warning: one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning: NaNs produced
#> Warning: one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning: NaNs produced
#> Warning: one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
coef(fit)
#> [1] 0.4828732