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Simulation studies

!!! note "Simulation study interface" We are currently working on an interface for simulation studies. Until we are finished with this, this page is just a collection of tips.

Swap observed data

In simulation studies, a common task is fitting the same model to many different datasets. It would be a waste of resources to reconstruct the complete model for each dataset. We therefore provide the function swap_observed to change the observed part of a model, without necessarily reconstructing the other parts.

For the A first model, you would use it as

using StructuralEquationModels

observed_vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8]
latent_vars = [:ind60, :dem60, :dem65]

graph = @StenoGraph begin

    # loadings
    ind60 → fixed(1)*x1 + x2 + x3
    dem60 → fixed(1)*y1 + y2 + y3 + y4
    dem65 → fixed(1)*y5 + y6 + y7 + y8

    # latent regressions
    ind60 → dem60
    dem60 → dem65
    ind60 → dem65

    # variances
    _(observed_vars) ↔ _(observed_vars)
    _(latent_vars) ↔ _(latent_vars)

    # covariances
    y1 ↔ y5
    y2 ↔ y4 + y6
    y3 ↔ y7
    y8 ↔ y4 + y6

end

partable = ParameterTable(
    graph,
    latent_vars = latent_vars, 
    observed_vars = observed_vars
)

data = example_data("political_democracy")

data_1 = data[1:30, :]

data_2 = data[31:75, :]

model = Sem(
    specification = partable,
    data = data_1
)

model_updated = swap_observed(model; data = data_2, specification = partable)

!!! danger "Thread safety" This is only relevant when you are planning to fit updated models in parallel

Models generated this way may share the same objects in memory (e.g. some parts of 
`model` and `model_updated` are the same objects in memory.)
Therefore, fitting both of these models in parallel will lead to **race conditions**, 
possibly crashing your computer.
To avoid these problems, you should copy `model` before updating it.

If you are building your models by parts, you can also update each part seperately with the function update_observed. For example,


new_observed = SemObservedData(;data = data_2, specification = partable)

my_optimizer = SemOptimizerOptim()

new_optimizer = update_observed(my_optimizer, new_observed)

API

swap_observed
update_observed