prep.measurement {dynr} | R Documentation |
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)
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. |
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.
Object of class 'dynrMeasurement'
Methods that can be used include: print
, printex
, show
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)))