rows(::SemObservedMissing) -> pattern_rows()

Author: alyst

src/frontend/fit/fitmeasures/minus2ll.jl

@@ -54,7 +54,7 @@ function minus2ll(observed::SemObservedMissing)
             observed.em_model.μ,
             observed.em_model.Σ,
             nsamples(observed),
-            observed.rows,
+            pattern_rows(observed),
             observed.patterns,
             observed.obs_mean,
             observed.obs_cov,
@@ -67,7 +67,7 @@ function minus2ll(observed::SemObservedMissing)
             observed.em_model.μ,
             observed.em_model.Σ,
             nsamples(observed),
-            observed.rows,
+            pattern_rows(observed),
             observed.patterns,
             observed.obs_mean,
             observed.obs_cov,

src/loss/ML/FIML.jl

@@ -50,7 +50,7 @@ function SemFIML(; observed, specification, kwargs...)
     inverses = broadcast(x -> zeros(x, x), pattern_nobs_vars(observed))
     choleskys = Array{Cholesky{Float64, Array{Float64, 2}}, 1}(undef, length(inverses))
 
-    n_patterns = size(rows(observed), 1)
+    n_patterns = size(pattern_rows(observed), 1)
     logdets = zeros(n_patterns)
 
     imp_mean = zeros.(pattern_nobs_vars(observed))
@@ -89,7 +89,7 @@ function objective!(semfiml::SemFIML, params, model)
 
     prepare_SemFIML!(semfiml, model)
 
-    objective = F_FIML(rows(observed(model)), semfiml, model, params)
+    objective = F_FIML(pattern_rows(observed(model)), semfiml, model, params)
     return objective / nsamples(observed(model))
 end
 
@@ -100,7 +100,7 @@ function gradient!(semfiml::SemFIML, params, model)
 
     prepare_SemFIML!(semfiml, model)
 
-    gradient = ∇F_FIML(rows(observed(model)), semfiml, model) / nsamples(observed(model))
+    gradient = ∇F_FIML(pattern_rows(observed(model)), semfiml, model) / nsamples(observed(model))
     return gradient
 end
 
@@ -112,8 +112,8 @@ function objective_gradient!(semfiml::SemFIML, params, model)
     prepare_SemFIML!(semfiml, model)
 
     objective =
-        F_FIML(rows(observed(model)), semfiml, model, params) / nsamples(observed(model))
-    gradient = ∇F_FIML(rows(observed(model)), semfiml, model) / nsamples(observed(model))
+        F_FIML(pattern_rows(observed(model)), semfiml, model, params) / nsamples(observed(model))
+    gradient = ∇F_FIML(pattern_rows(observed(model)), semfiml, model) / nsamples(observed(model))
 
     return objective, gradient
 end

src/observed/EM.jl

@@ -38,7 +38,7 @@ function em_mvn(
 
     ### precompute for full cases
     if length(observed.patterns[1]) == nvars
-        for row in observed.rows[1]
+        for row in pattern_rows(observed)[1]
             row = observed.data_rowwise[row]
             𝔼x_pre += row
             𝔼xxᵀ_pre += row * row'
@@ -107,7 +107,7 @@ function em_mvn_Estep!(𝔼x, 𝔼xxᵀ, em_model, observed, 𝔼x_pre, 𝔼xx
         V = Σ[u, u] - Σ[u, o] * (Σ[o, o] \ Σ[o, u])
 
         # loop trough data
-        for row in observed.rows[i]
+        for row in pattern_rows(observed)[i]
             m = μ[u] + Σ[u, o] * (Σ[o, o] \ (observed.data_rowwise[row] - μ[o]))
 
             𝔼xᵢ[u] = m

src/observed/missing.jl

@@ -35,7 +35,7 @@ For observed data with missing values.
 
 - `patterns(::SemObservedMissing)` -> indices of non-missing variables per missing patterns
 - `patterns_not(::SemObservedMissing)` -> indices of missing variables per missing pattern
-- `rows(::SemObservedMissing)` -> row indices of observed data points that belong to each pattern
+- `pattern_rows(::SemObservedMissing)` -> row indices of observed data points that belong to each pattern
 - `pattern_nsamples(::SemObservedMissing)` -> number of data points per pattern
 - `pattern_nobs_vars(::SemObservedMissing)` -> number of non-missing observed variables per pattern
 - `obs_mean(::SemObservedMissing)` -> observed mean per pattern
@@ -72,7 +72,7 @@ mutable struct SemObservedMissing{
     nsamples::O
     patterns::P # missing patterns
     patterns_not::P2
-    rows::R # coresponding rows in data_rowwise
+    pattern_rows::R # coresponding rows in data_rowwise
     data_rowwise::PD # list of data
     pattern_nsamples::PO # observed rows per pattern
     pattern_nobs_vars::PVO # number of non-missing variables per pattern
@@ -213,7 +213,7 @@ nobserved_vars(observed::SemObservedMissing) = observed.nobs_vars
 
 patterns(observed::SemObservedMissing) = observed.patterns
 patterns_not(observed::SemObservedMissing) = observed.patterns_not
-rows(observed::SemObservedMissing) = observed.rows
+pattern_rows(observed::SemObservedMissing) = observed.pattern_rows
 data_rowwise(observed::SemObservedMissing) = observed.data_rowwise
 pattern_nsamples(observed::SemObservedMissing) = observed.pattern_nsamples
 pattern_nobs_vars(observed::SemObservedMissing) = observed.pattern_nobs_vars