ParameterIdentification

Performs parameter estimation by fitting model simulations to observed data. Supports customizable optimization and confidence interval methods.

Active bindings

simulations

A named list of Simulation objects, keyed by the IDs of their root containers.

parameters

A list of PIParameters, each representing a grouped set of model parameters to be optimized (read-only).

configuration

A PIConfiguration object controlling algorithm, CI estimation, and objective function options.

outputMappings

A list of PIOutputMapping objects mapping observed datasets to simulated outputs.

Methods

Public methods

• ParameterIdentification$new()

• ParameterIdentification$run()

• ParameterIdentification$estimateCI()

• ParameterIdentification$plotResults()

• ParameterIdentification$gridSearch()

• ParameterIdentification$calculateOFVProfiles()

• ParameterIdentification$print()

---

Method new()

Initializes a ParameterIdentification instance.

Usage

ParameterIdentification$new(
  simulations,
  parameters,
  outputMappings,
  configuration = NULL
)

Arguments

simulations

A Simulation or list of Simulation objects to be used for parameter estimation. Each simulation must contain the model parameters specified in parameters. Use ospsuite::loadSimulation() to load simulation files.

parameters

A PIParameters or list of PIParameters objects specifying the model parameters to optimize. Each PIParameters object may group one or more underlying model parameters. See PIParameters for details.

outputMappings

A PIOutputMapping or list of PIOutputMapping objects mapping model outputs (represented by Quantity objects) to observed data. #' See PIOutputMapping for details.

configuration

(Optional) A PIConfiguration object specifying algorithm, CI method, and objective function settings. Defaults to a new configuration if omitted. See PIConfiguration for configuration options.

Returns

A ParameterIdentification object ready to run parameter estimation. Executes Parameter Identification

---

Method run()

Runs parameter identification using the configured optimization algorithm. Returns a structured piResultsobject containing estimated parameters, diagnostics, and (optionally) confidence intervals.

Usage

ParameterIdentification$run()

Returns

A PIResult object containing the optimization results. Estimate Confidence Intervals

---

Method estimateCI()

Computes confidence intervals for the optimized parameters using the method defined in the associated PIConfiguration. Intended for advanced use when autoEstimateCI was set to FALSE during the initial run.

Usage

ParameterIdentification$estimateCI()

Returns

The same PIResult object returned by the run() method, updated to include confidence interval estimates. Plot Parameter Estimation Results

---

Method plotResults()

Re-runs model simulations using the current or specified parameter values and generates plots comparing predictions to observed data.

Usage

ParameterIdentification$plotResults(par = NULL)

Arguments

par

Optional parameter values for simulations, in the order of ParameterIdentification$parameters. Use current values if NULL.

Returns

A list of ggplot2 plots (one per output mapping), showing:

• Individual time profiles

• Observed vs. simulated values

• Residuals vs. time Perform a Parameter Grid Search

Generates a grid of parameter combinations, computes the OFV for each, and optionally sets the best result as the starting point for s subsequent optimization.

Note: The resulting grid can be used to explore the parameter space or initialize better starting values.

---

Method gridSearch()

Usage

ParameterIdentification$gridSearch(
  lower = NULL,
  upper = NULL,
  logScaleFlag = FALSE,
  totalEvaluations = 50,
  setStartValue = FALSE
)

Arguments

lower

Numeric vector of parameter lower bounds, defaulting to PIParameter minimum values.

upper

Numeric vector of parameter upper bounds, defaulting to PIParameter maximum values.

logScaleFlag

Logical scalar or vector; determines if grid points are spaced logarithmically. Default is FALSE.

totalEvaluations

Integer specifying the total grid points. Default is 50.

setStartValue

Logical. If TRUE, updates PIParameter starting values to the best grid point. Default is FALSE.

Returns

A tibble where each row is a parameter combination and the corresponding objective function value (ofv). Calculate Objective Function Value (OFV) Profiles

Generates OFV profiles by varying each parameter independently while holding others constant.

---

Method calculateOFVProfiles()

Usage

ParameterIdentification$calculateOFVProfiles(
  par = NULL,
  boundFactor = 0.1,
  totalEvaluations = 20
)

Arguments

par

Numeric vector of parameter values, one for each parameter. Defaults to current parameter values if NULL, invalid or mismatched.

boundFactor

Numeric value. A value of 0.1 means lower is 10% below par and upper is 10% above par. Default is 0.1.

totalEvaluations

Integer specifying the total number of grid points across each parameter profile. Default is 20.

Returns

A list of tibbles, one for each parameter, showing how the objective function value (OFV) changes when varying that parameter while keeping the others fixed.

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Method print()

Prints a summary of ParameterIdentification instance.

Usage

ParameterIdentification$print()