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, ...)
# S3 method for ldmppr_fit
nobs(object, ...)
# S3 method for ldmppr_fit
as.data.frame(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, 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.
nobs(ldmppr_fit): Number of observations used in the fitted model.
as.data.frame(ldmppr_fit): Coerce fit summary to a one-row data frame.
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