MLE solver for dfr_dist objects — fit.dfr

Returns a solver function that fits a dfr_dist model to survival data using maximum likelihood estimation. The solver uses gradient-based optimization (BFGS by default) with analytical or numerical gradients.

Usage

# S3 method for class 'dfr_dist'
fit(object, ...)

Arguments

object: A dfr_dist object
...: Additional arguments passed to the log-likelihood, score, and Hessian constructors (e.g., integration parameters for cum_haz)

Value

A solver function that accepts:

df: Data frame with observation times and censoring indicators
par: Initial parameter values (uses object's params if NULL)
method: Optimization method (default "BFGS")
control: Control parameters for optim()
...: Additional arguments passed to likelihood functions

Details

The solver returns a fisher_mle object (from likelihood.model) containing:

Parameter estimates
Log-likelihood value at MLE
Variance-covariance matrix (from Hessian)
Convergence status

Use methods like coef(), vcov(), confint(), summary() on the result.

Examples

# \donttest{
# Exponential distribution
exp_dist <- dfr_dist(
  rate = function(t, par, ...) rep(par[1], length(t)),
  par = c(lambda = 1)
)

# Simulate data
set.seed(42)
df <- data.frame(t = rexp(100, rate = 2), delta = 1)

# Fit model
solver <- fit(exp_dist)
result <- solver(df, par = c(1))
summary(result)
#> Maximum Likelihood Estimate (Fisherian)
#> ----------------------------------------
#> 
#> Coefficients:
#>      Estimate Std. Error   2.5% 97.5%
#> [1,]   1.7789     0.1779 1.4302 2.128
#> 
#> Log-likelihood: -42.4 
#> AIC: 86.8 
#> Number of observations: 100 
confint(result)
#>          2.5%   97.5%
#> [1,] 1.430217 2.12752
# }