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Create full control list for initial parameter estimation

Source: R/initsControl.R
initsControl.Rd

Aggregates modular control functions into a structured list for use in population pharmacokinetic parameter initialization.

Usage

initsControl(
  ss.control = ss_control(),
  pooled.control = pooled_control(),
  nca.control = nca_control(),
  fallback.control = fallback_control(),
  selmetrics = "rRMSE2",
  hybrid.base = TRUE,
  preferNCA = TRUE
)

Arguments

ss.control

A control list consistent with the structure returned by ss_control().

pooled.control

A control list consistent with the structure returned by pooled_control().

nca.control

A control list consistent with the structure returned by nca_control().

fallback.control

A control list consistent with the structure returned by fallback_control().

selmetrics

A character string or vector specifying model performance metrics to evaluate. Must be one or more of "APE", "MAE", "MAPE", "RMSE", "rRMSE1", or "rRMSE2". Default is "rRMSE2".

hybrid.base

Logical. If TRUE, enables hybrid evaluation mode in which model performance is assessed using mixed parameter combinations across methods. If FALSE, each method is evaluated independently. Default is TRUE.

preferNCA

Logical. If TRUE and selmetrics equals "rRMSE2", the lowest rRMSE2 is selected first. If the best-performing method is not NCA-based, the function then checks whether an NCA-based method offers a lower rRMSE1. If so, the NCA method is selected. Default is TRUE.

Value

A named list combining all control modules for parameter estimation.

See also

ss_control, pooled_control, nca_control, fallback_control

Examples

initsControl(
  pooled.control = pooled_control(nbins = 8),
  fallback.control = fallback_control(
    sigma_method_additive = "fixed_fraction"
  )
)
#> $ss.control
#> $ss.control$ss_method
#> [1] "combined"
#> 
#> $ss.control$no.doses
#> [1] 5
#> 
#> $ss.control$no.half_lives
#> [1] 5
#> 
#> $ss.control$allowed_interval_variation
#> [1] 0.25
#> 
#> $ss.control$allowed_dose_variation
#> [1] 0.2
#> 
#> $ss.control$min_doses_required
#> [1] 3
#> 
#> $ss.control$tad_rounding
#> [1] TRUE
#> 
#> 
#> $pooled.control
#> $pooled.control$nbins
#> [1] 8
#> 
#> $pooled.control$bin_method
#> [1] "quantile"
#> 
#> $pooled.control$tad_rounding
#> [1] TRUE
#> 
#> 
#> $nca.control
#> $nca.control$trapezoidal.rule
#> [1] "linear_up_log_down" "linear"            
#> 
#> $nca.control$duration
#> NULL
#> 
#> $nca.control$nlastpoints
#> [1] 3
#> 
#> $nca.control$slope.method
#> [1] "bestfitforce"
#> 
#> 
#> $fallback.control
#> $fallback.control$enable_ka_fallback
#> [1] TRUE
#> 
#> $fallback.control$sigma_method_additive
#> [1] "fixed_fraction"
#> 
#> $fallback.control$sigma_method_proportional
#> [1] "model"
#> 
#> $fallback.control$sigma_fallback_fraction
#> [1] 0.2
#> 
#> 
#> $selmetrics
#> [1] "rRMSE2"
#> 
#> $hybrid.base
#> [1] TRUE
#> 
#> $preferNCA
#> [1] TRUE
#>