prep.matrixDynamics: Recipe function for creating Linear Dynamics using matrices

In dynr: Dynamic Models with Regime-Switching

Recipe function for creating Linear Dynamics using matrices

Description

Recipe function for creating Linear Dynamics using matrices

Usage

prep.matrixDynamics(params.dyn = NULL, values.dyn, params.exo = NULL,
  values.exo = NULL, params.int = NULL, values.int = NULL, covariates,
  isContinuousTime)

Arguments

params.dyn	the matrix of parameter names for the transition matrix in the specified linear dynamic model
values.dyn	the matrix of starting/fixed values for the transition matrix in the specified linear dynamic model
params.exo	the matrix of parameter names for the regression slopes of covariates on the latent variables (see details)
values.exo	matrix of starting/fixed values for the regression slopes of covariates on the latent variables (see details)
params.int	vector of names for intercept parameters in the dynamic model specified as a matrix or list of matrices.
values.int	vector of intercept values in the dynamic model specified as matrix or list of matrices. Contains starting/fixed values of the intercepts.
covariates	the names or the index numbers of the covariates used in the dynamic model
isContinuousTime	logical. When TRUE, use a continuous time model. When FALSE use a discrete time model.

Details

A recipe function for specifying the deterministic portion of a set of linear dynamic functions as:

Discrete-time model: eta(t+1) = int + dyn*eta(t) + exo*x(t), where eta(t) is a vector of latent variables, x(t) is a vector of covariates, int, dyn, and exo are vectors and matrices specified via the arguments *.int, *.dyn, and *.exo.

Continuous-time model: d/dt eta(t) = int + dyn*eta(t) + exo*x(t), where eta(t) is a vector of latent variables, x(t) is a vector of covariates, int, dyn, and exo are vectors and matrices specified via the arguments *.int, *.dyn, and *.exo.

The left-hand side of the dynamic model consists of a vector of latent variables for the next time point in the discrete-time case, and the vector of derivatives for the latent variables at the current time point in the continuous-time case.

For models with regime-switching dynamic functions, the user will need to provide a list of the *.int, *.dyn, and *.exo arguments. (when they are specified to take on values other than the default of zero vectors and matrices), or if a single set of vectors/matrices are provided, the same vectors/matrices are assumed to hold across regimes.

prep.matrixDynamics serves as an alternative to prep.formulaDynamics.

Value

Object of class 'dynrDynamicsMatrix'

See Also

Methods that can be used include: print, show

Examples

#Single-regime, continuous-time model. For further details run: 
#demo(RSNonlinearDiscrete, package="dynr"))
dynamics <- prep.matrixDynamics(
    values.dyn=matrix(c(0, -0.1, 1, -0.2), 2, 2),
    params.dyn=matrix(c('fixed', 'spring', 'fixed', 'friction'), 2, 2),
    isContinuousTime=TRUE)

#Two-regime, continuous-time model. For further details run: 
#demo(RSNonlinearDiscrete, package="dynr"))
dynamics <- prep.matrixDynamics(
    values.dyn=list(matrix(c(0, -0.1, 1, -0.2), 2, 2),
                    matrix(c(0, -0.1, 1, 0), 2, 2)),
    params.dyn=list(matrix(c('fixed', 'spring', 'fixed', 'friction'), 2, 2),
                    matrix(c('fixed', 'spring', 'fixed', 'fixed'), 2, 2)),
    isContinuousTime=TRUE) 

dynr documentation built on May 29, 2024, 2:49 a.m.