Estimation settings for tseLCA models — tse

Collects the numerical settings of the three estimation steps. Pass the result as the control argument of the model-fitting functions.

Usage

tse_control(
  step1.maxit = 5000L,
  step1.tol = 1e-08,
  step1.restarts = 10L,
  step1.restart.R2 = 0.7,
  n_init = NULL,
  step3.maxit = 200L,
  step3.tol = 1e-06,
  boundary.tol = 0.01,
  hessian = c("observed", "opg"),
  verbose = FALSE
)

Arguments

step1.maxit: Maximum number of EM iterations for the Step-1 measurement model. A fit that reaches it is retried with twice as many.
step1.tol: Convergence tolerance of the Step-1 EM algorithm (change in log-likelihood).
step1.restarts: Number of additional random starts tried when the default Step-1 fit has an entropy R\(^2\) below step1.restart.R2; the fit with the highest log-likelihood is kept.
step1.restart.R2: Entropy R\(^2\) threshold that triggers step1.restarts.
n_init: Optional number of Step-1 fits from independent random classifications, bypassing the default k-means initialization; the fit with the highest log-likelihood is kept. Unlike step1.restarts, these always run. NULL (default) uses the default initialization.
step3.maxit: Maximum number of iterations for the Step-3 (structural model) EM or Newton-Raphson algorithm.
step3.tol: Convergence tolerance of the Step-3 algorithm.
boundary.tol: Step-1 probabilities within this distance of 0 or 1 are treated as fixed when computing the Step-1 variance.
hessian: Step-3 information matrix used for standard errors: "observed" (default), or "opg" for the outer product of the case-wise scores. "opg" applies to ML covariate models.
verbose: Logical. Print progress and convergence messages.

Value

A list of class "tse_control".

Examples

tse_control()
#> tseLCA estimation settings
#>                  value   
#> step1.maxit      5000    
#> step1.tol        1e-08   
#> step1.restarts   10      
#> step1.restart.R2 0.7     
#> n_init           NULL    
#> step3.maxit      200     
#> step3.tol        1e-06   
#> boundary.tol     0.01    
#> hessian          observed
#> verbose          FALSE   
tse_control(step1.maxit = 10000, n_init = 20)
#> tseLCA estimation settings
#>                  value   
#> step1.maxit      10000   
#> step1.tol        1e-08   
#> step1.restarts   10      
#> step1.restart.R2 0.7     
#> n_init           20      
#> step3.maxit      200     
#> step3.tol        1e-06   
#> boundary.tol     0.01    
#> hessian          observed
#> verbose          FALSE