This function always returns degradation model parameters as well as error model parameters, in order to avoid working with a fitted model without considering the error structure that was assumed for the fit.

parms(object, ...)

# S3 method for mkinfit
parms(object, transformed = FALSE, ...)

Arguments

object

A fitted model object

...

Not used

transformed

Should the parameters be returned as used internally during the optimisation?

Value

A numeric vector of fitted model parameters

Examples

fit <- mkinfit("SFO", FOCUS_2006_C)
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 2388.077 #> Sum of squared residuals at call 3: 2388.077 #> Sum of squared residuals at call 4: 247.1962 #> Sum of squared residuals at call 7: 200.6791 #> Sum of squared residuals at call 10: 197.7231 #> Sum of squared residuals at call 11: 197.0872 #> Sum of squared residuals at call 14: 196.535 #> Sum of squared residuals at call 15: 196.535 #> Sum of squared residuals at call 16: 196.535 #> Sum of squared residuals at call 17: 196.5334 #> Sum of squared residuals at call 20: 196.5334 #> Sum of squared residuals at call 25: 196.5334 #> Negative log-likelihood at call 31: 26.64668
#> Optimisation successfully terminated.
parms(fit)
#> parent_0 k_parent_sink sigma #> 82.4921598 0.3060633 4.6730124
parms(fit, transformed = TRUE)
#> parent_0 log_k_parent_sink sigma #> 82.492160 -1.183963 4.673012