lavaan (version 0.6-19)

lav_export_estimation: lav_export_estimation

Description

lavaan provides a range of optimization methods with the optim.method argument (nlminb, BFGS, L-BFGS-B, GN, and nlminb.constr). `lav_export_estimation` allows exporting objects and functions necessary to pass a lavaan model into any optimizer that takes a combination of (1) starting values, (2) fit-function, (3) gradient-function, and (4) upper and lower bounds. This allows testing new optimization frameworks.

Usage

lav_export_estimation(lavaan_model)

Value

List with:

• get_coef - When working with equality constraints, lavaan internally uses some transformations. get_coef is a functions that recreates the coef function for the parameters.

• starting_values - starting_values to be used in the optimization

• objective_function - objective function, expecting the current parameter values and the lavaan model

• gradient_function - gradient function, expecting the current parameter values and the lavaan model

• lower - lower bounds for parameters

• upper - upper bound for parameters

Arguments

lavaan_model

a fitted lavaan model

Examples

library(lavaan)
model <- ' 
  # latent variable definitions
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + y2 + y3 + y4
     dem65 =~ y5 + a*y6 + y7 + y8

  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60
'

fit <- sem(model, 
           data = PoliticalDemocracy, 
           do.fit = FALSE)

est <- lav_export_estimation(lavaan_model = fit)

# The starting values are:
est$starting_values
# Note that these do not have labels (and may also differ from coef(fit)
# in case of equality constraints):
coef(fit)
# To get the same parameters, use:
est$get_coef(parameter_values = est$starting_values, 
             lavaan_model = fit)

# The objective function can be used to compute the fit at the current estimates:
est$objective_function(parameter_values = est$starting_values, 
                       lavaan_model = fit)

# The gradient function can be used to compute the gradients at the current estimates:
est$gradient_function(parameter_values = est$starting_values, 
                      lavaan_model = fit)

# Together, these elements provide the means to estimate the parameters with a large
# range of optimizers. For simplicity, here is an example using optim:
est_fit <- optim(par = est$starting_values, 
                 fn = est$objective_function,
                 gr = est$gradient_function,
                 lavaan_model = fit,
                 method = "BFGS")
est$get_coef(parameter_values = est_fit$par,
             lavaan_model = fit)

# This is identical to
coef(sem(model, 
         data = PoliticalDemocracy))

# Example using ridge regularization for parameter a
fn_ridge <- function(parameter_values, lavaan_model, est, lambda){
  return(est$objective_function(parameter_values = parameter_values, 
                                lavaan_model = lavaan_model) + lambda * parameter_values[6]^2)
}
ridge_fit <- optim(par = est$get_coef(est$starting_values,
                                      lavaan_model = fit), 
                   fn = fn_ridge, 
                   lavaan_model = fit,
                   est = est,
                   lambda = 10)
est$get_coef(parameter_values = ridge_fit$par,
             lavaan_model = fit)