adapt mg models and fitmeasures

Author: Maximilian-Stefan-Ernst

src/additional_functions/helper.jl

@@ -119,7 +119,7 @@ end
 check_single_lossfun(model::SemEnsemble; throw_error) = 
     check_single_lossfun(model.sems...; throw_error)
 
-# sclaing corrections for fit measures and multigroup models
-dof_correction(::SemFIML) = 0
-dof_correction(::SemML) = -1
-dof_correction(::SemWLS) = -1
+# scaling corrections for multigroup models
+mg_correction(::SemFIML) = 0
+mg_correction(::SemML) = 0
+mg_correction(::SemWLS) = -1

src/frontend/fit/fitmeasures/RMSEA.jl

@@ -8,19 +8,23 @@ function RMSEA end
 RMSEA(fit::SemFit) = RMSEA(fit, fit.model)
 
 function RMSEA(fit::SemFit, model::AbstractSemSingle)
-    check_uniform_lossfun(model)
-    return RMSEA(dof(fit), χ²(fit), nsamples(fit)-dof_correction(model.loss.functions[1]))
+    check_single_lossfun(model; throw_error = true)
+    return RMSEA(dof(fit), χ²(fit), nsamples(fit)+rmsea_correction(model.loss.functions[1]))
 end
 
 function RMSEA(fit::SemFit, model::SemEnsemble)
     check_single_lossfun(model; throw_error = true)
-    n = nsamples(fit)-model.n*dof_correction(model.sems[1].loss.functions[1])
+    n = nsamples(fit)+model.n*rmsea_correction(model.sems[1].loss.functions[1])
     return sqrt(length(model.sems)) * RMSEA(dof(fit), χ²(fit), n)
 end
 
-function RMSEA(dof, chi2, c)
-    rmsea = (chi2 - dof) / (c * dof)
+function RMSEA(dof, chi2, N⁻)
+    rmsea = (chi2 - dof) / (N⁻ * dof)
     rmsea = rmsea > 0 ? rmsea : 0
     return sqrt(rmsea)
 end
 
+# scaling corrections
+rmsea_correction(::SemFIML) = 0
+rmsea_correction(::SemML) = -1
+rmsea_correction(::SemWLS) = -1

src/frontend/fit/fitmeasures/chi2.jl

@@ -16,7 +16,7 @@ end
 
 χ²(::SemML, fit::SemFit, model::AbstractSemSingle) =
     (nsamples(fit) - 1) *
-    (fit.minimum - logdet(obs_cov(observed(model))) - nobserved_vars(observed(model)))
+    (fit.minimum - logdet(obs_cov(observed(model))) - nobserved_vars(model))
 
 # bollen, p. 115, only correct for GLS weight matrix
 χ²(::SemWLS, fit::SemFit, model::AbstractSemSingle) =
@@ -44,11 +44,14 @@ function χ²(::SemWLS, fit::SemFit, models::SemEnsemble)
 end
 
 function χ²(::SemML, fit::SemFit, models::SemEnsemble)
-    G = sum(zip(models.weights, models.sems)) do (w, model)
-        data = observed(model)
-        w * (logdet(obs_cov(data)) + nobserved_vars(data))
+    F = 0
+    for model in models.sems
+        Fᵢ = objective(model, fit.solution)
+        Fᵢ -= logdet(obs_cov(observed(model))) + nobserved_vars(model)
+        Fᵢ *= nsamples(model) - 1
+        F += Fᵢ
     end
-    return (nsamples(models) - models.n) * (fit.minimum - G)
+    return F
 end
 
 function χ²(::SemFIML, fit::SemFit, models::SemEnsemble)

src/frontend/fit/fitmeasures/minus2ll.jl

@@ -11,23 +11,23 @@ minus2ll(fit::SemFit) = minus2ll(fit, fit.model)
 
 function minus2ll(fit::SemFit, model::AbstractSemSingle)
     check_single_lossfun(model; throw_error = true)
-    return minus2ll(model.loss.functions[1], fit, model)
+    F = objective(model, fit.solution)
+    return minus2ll(model.loss.functions[1], F, model)
 end
 
 # SemML ------------------------------------------------------------------------------------
-function minus2ll(::SemML, fit::SemFit, model::AbstractSemSingle)
-    obs = observed(model)
-    return nsamples(obs) * (fit.minimum + log(2π) * nobserved_vars(obs))
+function minus2ll(::SemML, F, model::AbstractSemSingle)
+    return nsamples(model) * (F + log(2π) * nobserved_vars(model))
 end
 
 # WLS --------------------------------------------------------------------------------------
-minus2ll(::SemWLS, ::SemFit, ::AbstractSemSingle) = missing
+minus2ll(::SemWLS, F, ::AbstractSemSingle) = missing
 
 # compute likelihood for missing data - H0 -------------------------------------------------
 # -2ll = (∑ log(2π)*(nᵢ*mᵢ)) + F*n
-function minus2ll(::SemFIML, fit::SemFit, model::AbstractSemSingle)
+function minus2ll(::SemFIML, F, model::AbstractSemSingle)
     obs = observed(model)::SemObservedMissing
-    F = fit.minimum * nsamples(obs)
+    F *= nsamples(obs)
     F += log(2π) * sum(pat -> nsamples(pat) * nmeasured_vars(pat), obs.patterns)
     return F
 end

src/types.jl

@@ -235,13 +235,13 @@ function multigroup_weights(models, n)
         return [(nsamples(model)) / (nsamples_total) for model in models]
     end
     lossfun = models[1].loss.functions[1]
-    if !applicable(dof_correction, lossfun)
+    if !applicable(mg_correction, lossfun)
         @info "We don't know how to choose group weights for the specified loss function.
                 Default weights of (#samples per group/#total samples) will be used".
         return [(nsamples(model)) / (nsamples_total) for model in models]
     end
-    dc = dof_correction(lossfun)
-    return [(nsamples(model)-dc) / (nsamples_total-n*dc) for model in models]
+    c = mg_correction(lossfun)
+    return [(nsamples(model)+c) / (nsamples_total+n*c) for model in models]
 end
 
 param_labels(ensemble::SemEnsemble) = ensemble.param_labels