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Model fitting

As we saw in A first model, after you have build a model, you can fit it via

model_fit = fit(model)

# output

Fitted Structural Equation Model
===============================================
--------------------- Model -------------------

Structural Equation Model
- Loss Functions
   SemML
- Fields
   observed:  SemObservedData
   implied:   RAM
   optimizer: SemOptimizerOptim

------------- Optimization result -------------

 * Status: success

 * Candidate solution
    Final objective value:     2.120543e+01

 * Found with
    Algorithm:     L-BFGS

 * Convergence measures
    |x - x'|               = 6.13e-05 ≰ 1.5e-08
    |x - x'|/|x'|          = 8.21e-06 ≰ 0.0e+00
    |f(x) - f(x')|         = 1.05e-09 ≰ 0.0e+00
    |f(x) - f(x')|/|f(x')| = 4.94e-11 ≤ 1.0e-10
    |g(x)|                 = 2.48e-05 ≰ 1.0e-08

 * Work counters
    Seconds run:   0  (vs limit Inf)
    Iterations:    175
    f(x) calls:    524
    ∇f(x) calls:   524

Choosing an optimizer

To choose a different optimizer, you can call fit with the keyword argument engine = ..., and pass additional keyword arguments:

using Optim

model_fit = fit(model; engine = :Optim, algorithm = BFGS())

Available options for engine are :Optim, :NLopt and :Proximal, where :NLopt and :Proximal are only available if the NLopt.jl and ProximalAlgorithms.jl packages are loaded respectively.

The available keyword arguments are listed in the sections Using Optim.jl, Using NLopt.jl and Regularization.

Alternative, you can also explicitely define a SemOptimizer and pass it as the first argument to fit:

my_optimizer = SemOptimizerOptim(algorithm = BFGS())

fit(my_optimizer, model)

You may also optionally specify Starting values.

API - model fitting

fit