prep.measurement function

Prepare the measurement recipe

Prepare the measurement recipe

prep.measurement(values.load, params.load = NULL, values.exo = NULL, params.exo = NULL, values.int = NULL, params.int = NULL, obs.names, state.names, exo.names)

Arguments

  • values.load: matrix of starting or fixed values for factor loadings. For models with regime-specific factor loadings provide a list of matrices of factor loadings.
  • params.load: matrix or list of matrices. Contains parameter names of the factor loadings.
  • values.exo: matrix or list of matrices. Contains starting/fixed values of the covariate regression slopes.
  • params.exo: matrix or list of matrices. Parameter names of the covariate regression slopes.
  • values.int: vector of intercept values specified as matrix or list of matrices. Contains starting/fixed values of the intercepts.
  • params.int: vector of names for intercept parameters specified as a matrix or list of matrices.
  • obs.names: vector of names for the observed variables in the order they appear in the measurement model.
  • state.names: vector of names for the latent variables in the order they appear in the measurement model.
  • exo.names: (optional) vector of names for the exogenous variables in the order they appear in the measurement model.

Returns

Object of class 'dynrMeasurement'

Details

The values.* arguments give the starting and fixed values for their respective matrices. The params.* arguments give the free parameter labels for their respective matrices. Numbers can be used as labels. The number 0 and the character 'fixed' are reserved for fixed parameters.

When a single matrix is given to values.*, that matrix is not regime-switching. Correspondingly, when a list of length r is given, that matrix is regime-switching with values and params for the r regimes in the elements of the list.

Examples

prep.measurement(diag(1, 5), diag("lambda", 5)) prep.measurement(matrix(1, 5, 5), diag(paste0("lambda_", 1:5))) prep.measurement(diag(1, 5), diag(0, 5)) #identity measurement model #Regime-switching measurement model where the first latent variable is # active for regime 1, and the second latent variable is active for regime 2 # No free parameters are present. prep.measurement(values.load=list(matrix(c(1,0), 1, 2), matrix(c(0, 1), 1, 2)))

See Also

Methods that can be used include: print, printex, show

  • Maintainer: Michael D. Hunter
  • License: GPL-3
  • Last published: 2023-11-28