format

Author: Maximilian-Stefan-Ernst

src/additional_functions/simulation.jl

@@ -99,7 +99,6 @@ function update_observed(loss::SemLoss, new_observed; kwargs...)
     return SemLoss(new_functions, loss.weights)
 end
 
-
 ############################################################################################
 # simulate data
 ############################################################################################
@@ -121,24 +120,22 @@ rand(model, start_simple(model), 100)
 ```
 """
 function Distributions.rand(
-        model::AbstractSemSingle{O, I, L, D}, 
-        params, 
-        n::Integer) where {O, I <: Union{RAM, RAMSymbolic}, L, D}
-    update!(
-        EvaluationTargets{true, false, false}(),
-        model.imply,
-        model,
-        params)
+    model::AbstractSemSingle{O, I, L, D},
+    params,
+    n::Integer,
+) where {O, I <: Union{RAM, RAMSymbolic}, L, D}
+    update!(EvaluationTargets{true, false, false}(), model.imply, model, params)
     return rand(model, n)
 end
 
 function Distributions.rand(
-        model::AbstractSemSingle{O, I, L, D},
-        n::Integer) where {O, I <: Union{RAM, RAMSymbolic}, L, D}
+    model::AbstractSemSingle{O, I, L, D},
+    n::Integer,
+) where {O, I <: Union{RAM, RAMSymbolic}, L, D}
     if MeanStruct(model.imply) === NoMeanStruct
         data = permutedims(rand(MvNormal(Symmetric(model.imply.Σ)), n))
     elseif MeanStruct(model.imply) === HasMeanStruct
         data = permutedims(rand(MvNormal(model.imply.μ, Symmetric(model.imply.Σ)), n))
     end
     return data
-end
+end

test/examples/political_democracy/constructor.jl

@@ -174,7 +174,8 @@ end
         start_regressions = 0.5,
         start_variances_observed = 0.5,
         start_variances_latent = 1.0,
-        start_covariances_observed = 0.2)
+        start_covariances_observed = 0.2,
+    )
     # set seed for simulation
     Random.seed!(83472834)
     colnames = Symbol.(names(example_data("political_democracy")))
@@ -183,13 +184,13 @@ end
         model_ml,
         data = rand(model_ml, params, 100_000),
         specification = spec,
-        obs_colnames = colnames
+        obs_colnames = colnames,
     )
     model_ml_sym_new = swap_observed(
         model_ml_sym,
         data = rand(model_ml_sym, params, 100_000),
         specification = spec,
-        obs_colnames = colnames
+        obs_colnames = colnames,
     )
     # fit models
     sol_ml = solution(sem_fit(model_ml_new))
@@ -370,7 +371,6 @@ end
     )
 end
 
-
 ############################################################################################
 ### data simulation
 ############################################################################################
@@ -384,7 +384,8 @@ end
         start_variances_observed = 0.5,
         start_variances_latent = 1.0,
         start_covariances_observed = 0.2,
-        start_means = 0.5)
+        start_means = 0.5,
+    )
     # set seed for simulation
     Random.seed!(83472834)
     colnames = Symbol.(names(example_data("political_democracy")))
@@ -394,14 +395,14 @@ end
         data = rand(model_ml, params, 100_000),
         specification = spec,
         obs_colnames = colnames,
-        meanstructure = true
+        meanstructure = true,
     )
     model_ml_sym_new = swap_observed(
         model_ml_sym,
         data = rand(model_ml_sym, params, 100_000),
         specification = spec,
         obs_colnames = colnames,
-        meanstructure = true
+        meanstructure = true,
     )
     # fit models
     sol_ml = solution(sem_fit(model_ml_new))