Objects of class `ldmppr_fit` are returned by [estimate_process_parameters()]. They contain the best-fitting optimization result (and optionally multiple fits, e.g. from a delta search) along with metadata used to reproduce the fit.
# S3 method for ldmppr_fit
print(x, ...)# S3 method for ldmppr_fit
coef(object, ...)
# S3 method for ldmppr_fit
logLik(object, ...)
# S3 method for ldmppr_fit
summary(object, ...)
# S3 method for summary.ldmppr_fit
print(x, ...)
# S3 method for ldmppr_fit
plot(x, ...)
as_nloptr(x, ...)
# S3 method for ldmppr_fit
as_nloptr(x, ...)
* `print()` prints a brief summary of the fit. * `coef()` returns the estimated parameter vector. * `logLik()` returns the log-likelihood at the optimum. * `summary()` returns a `summary.ldmppr_fit`. * `plot()` plots diagnostics for multi-fit runs (e.g. objective vs delta), if available.
an object of class `ldmppr_fit`.
additional arguments (not used).
an object of class `ldmppr_fit`.
print(ldmppr_fit): Print a brief summary of a fitted model.
coef(ldmppr_fit): Extract the estimated parameter vector.
logLik(ldmppr_fit): Log-likelihood at the optimum.
summary(ldmppr_fit): Summarize a fitted model.
plot(ldmppr_fit): Plot diagnostics for a fitted model.
as_nloptr(ldmppr_fit): Extract the underlying `nloptr` result.
print(summary.ldmppr_fit): Print a summary produced by [summary.ldmppr_fit()].
as_nloptr(): Extract the underlying `nloptr` result.
A `ldmppr_fit` is a list with (at minimum):
`process`: process name (e.g. `"self_correcting"`)
`fit`: best optimization result (currently an `nloptr` object)
`mapping`: mapping information (e.g. chosen `delta`, objectives)
`grid`: grid definitions used by likelihood approximation