Struct SDE 

pub struct SDE { /* private fields */ }

Stochastic Differential Equation solver for pharmacometric models.

This struct represents a stochastic differential equation system and provides methods to simulate particles and estimate likelihood for PKPD modeling.

SDE models introduce stochasticity into the system dynamics, allowing for more realistic modeling of biological variability and uncertainty.

Implementations

impl SDE

pub fn new( drift: fn(&NalgebraVec<f64>, &NalgebraVec<f64>, f64, &mut NalgebraVec<f64>, &NalgebraVec<f64>, &Covariates), diffusion: fn(&NalgebraVec<f64>, &mut NalgebraVec<f64>), lag: fn(&NalgebraVec<f64>, f64, &Covariates) -> HashMap<usize, f64>, fa: fn(&NalgebraVec<f64>, f64, &Covariates) -> HashMap<usize, f64>, init: fn(&NalgebraVec<f64>, f64, &Covariates, &mut NalgebraVec<f64>), out: fn(&NalgebraVec<f64>, &NalgebraVec<f64>, f64, &Covariates, &mut NalgebraVec<f64>), nparticles: usize, ) -> SDE

Creates a new stochastic differential equation solver with default Neqs.

Use builder methods to configure dimensions:

SDE::new(drift, diffusion, lag, fa, init, out, nparticles)
    .with_nstates(2)
    .with_ndrugs(1)
    .with_nout(1)

pub fn with_nstates(self, nstates: usize) -> SDE

Set the number of state variables.

pub fn with_ndrugs(self, ndrugs: usize) -> SDE

Set the number of drug input channels (size of bolus[] and rateiv[]).

pub fn with_nout(self, nout: usize) -> SDE

Set the number of output equations.

Trait Implementations

impl Clone for SDE

fn clone(&self) -> SDE

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SDE

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Equation for SDE

fn estimate_likelihood( &self, subject: &Subject, support_point: &Vec<f64>, error_models: &AssayErrorModels, ) -> Result<f64, PharmsolError>

👎Deprecated since 0.23.0: Use estimate_log_likelihood() instead for better numerical stability

Estimates the likelihood of observed data given a model and parameters.

Arguments

• subject - Subject data containing observations
• support_point - Parameter vector for the model
• error_model - Error model to use for likelihood calculations

Returns

The log-likelihood of the observed data given the model and parameters.

fn estimate_log_likelihood( &self, subject: &Subject, support_point: &Vec<f64>, error_models: &AssayErrorModels, ) -> Result<f64, PharmsolError>

Estimate the log-likelihood of the subject given the support point and error model.

fn kind() -> EqnKind

fn estimate_predictions( &self, subject: &Subject, support_point: &Vec<f64>, ) -> Result<Self::P, PharmsolError>

Generate predictions for a subject with given parameters.

fn nouteqs(&self) -> usize

Get the number of output equations in the model.

fn nstates(&self) -> usize

Get the number of state variables in the model.

fn simulate_subject( &self, subject: &Subject, support_point: &Vec<f64>, error_models: Option<&AssayErrorModels>, ) -> Result<(Self::P, Option<f64>), PharmsolError>

Simulate a subject with given parameters and optionally calculate likelihood.

impl EquationTypes for SDE

type S = Vec<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>>

The state vector type

type P = ArrayBase<OwnedRepr<Prediction>, Dim<[usize; 2]>>

The predictions container type