Update tests for identifiability warning messages

test/runtests.jl

@@ -9,71 +9,73 @@ using Statistics
 
 @testset "LatentClassAnalysis.jl" begin
     Random.seed!(123)  # For reproducibility
-    
+
     @testset "Data Preparation" begin
         # Create test data with sufficient variables
         df = DataFrame(
-            x1 = repeat([1, 2], 50),         # binary 1/2
-            x2 = repeat([0, 1], 50),         # binary 0/1
-            x3 = categorical(repeat(["A", "B", "C"], 34)[1:100]),  # 3 categories
-            x4 = repeat([1, 2], 50),         # binary 1/2
-            x5 = repeat([1, 2], 50)          # binary 1/2
+            x1=repeat([1, 2], 50),         # binary 1/2
+            x2=repeat([0, 1], 50),         # binary 0/1
+            x3=categorical(repeat(["A", "B", "C"], 34)[1:100]),  # 3 categories
+            x4=repeat([1, 2], 50),         # binary 1/2
+            x5=repeat([1, 2], 50)          # binary 1/2
         )
-        
+
         # Test basic data preparation
         data, n_cats = prepare_data(df, :x1, :x2, :x3, :x4, :x5)
         @test size(data) == (100, 5)
         @test n_cats == [2, 2, 3, 2, 2]
         @test all(x -> x ≥ 1, data)  # All values should be 1-based
-        
+
         # Test with single column
         data_single, n_cats_single = prepare_data(df, :x1)
         @test size(data_single) == (100, 1)
         @test n_cats_single == [2]
     end
-    
+
     @testset "Model Initialization" begin
         # Valid model (5 binary items, 2 classes)
         n_items = 5
         n_classes = 2
         n_categories = fill(2, n_items)
         model = LCAModel(n_classes, n_items, n_categories)
-        
+
         # Test model structure
         @test model.n_classes == 2
         @test model.n_items == 5
         @test length(model.class_probs) == 2
         @test length(model.item_probs) == 5
         @test all(isapprox(sum(model.class_probs), 1.0))
-        
+
         # Test invalid inputs
         @test_throws ArgumentError LCAModel(1, n_items, n_categories)  # < 2 classes
         @test_throws ArgumentError LCAModel(2, n_items, [1, 2, 2, 2, 2])  # < 2 categories
-        
+
         # Test identifiability conditions
-        @test_throws ArgumentError LCAModel(3, 2, [2, 2])  # insufficient items for 3 classes
+        @test_logs (:warn, "Model may not be identifiable. With 3 classes and minimum of 2 categories, need ideally 5 items (got 2).") begin
+            LCAModel(3, 2, [2, 2])
+        end
     end
-    
+
     @testset "Model Fitting" begin
         # Generate synthetic data with known structure
         n_samples = 100
         n_items = 5  # Sufficient for 2 classes with binary items
-        
+
         # Create data matrix
         data = zeros(Int, n_samples, n_items)
         true_classes = rand(1:2, n_samples)
-        
+
         for i in 1:n_samples
             for j in 1:n_items
                 p = true_classes[i] == 1 ? 0.8 : 0.2
                 data[i, j] = rand() < p ? 1 : 2
             end
         end
-        
+
         # Fit model
         model = LCAModel(2, n_items, fill(2, n_items))
         ll = fit!(model, data)
-        
+
         # Test results
         @test !isnan(ll)
         @test !isinf(ll)
@@ -84,30 +86,30 @@ using Statistics
             @test all(isapprox.(sum(item_prob, dims=2), 1.0, atol=1e-10))
         end
     end
-    
+
     @testset "Model Diagnostics" begin
         # Prepare data
         n_items = 5  # Sufficient for 2 classes
         df = DataFrame(
             [Symbol("x$i") => repeat([1, 2], 50) for i in 1:n_items]...
         )
         data, n_cats = prepare_data(df, [Symbol("x$i") for i in 1:n_items]...)
-        
+
         # Fit model
         model = LCAModel(2, n_items, n_cats)
         ll = fit!(model, data)
-        
+
         # Calculate diagnostics
         diag = diagnostics!(model, data, ll)
-        
+
         # Test diagnostic values
         @test !isnan(diag.ll)
         @test !isnan(diag.aic)
         @test !isnan(diag.bic)
         @test !isnan(diag.sbic)
         @test diag.bic > diag.aic  # BIC should be more conservative
     end
-    
+
     @testset "Prediction" begin
         # Prepare data
         n_items = 5  # Sufficient for 2 classes
@@ -116,47 +118,47 @@ using Statistics
         for i in 1:n_samples
             data[i, :] .= rand(1:2, n_items)
         end
-        
+
         # Fit model and predict
         model = LCAModel(2, n_items, fill(2, n_items))
         fit!(model, data)
         assignments, probs = predict(model, data)
-        
+
         # Test predictions
         @test length(assignments) == n_samples
         @test size(probs) == (n_samples, 2)
         @test all(1 .<= assignments .<= 2)
         @test all(0 .<= probs .<= 1)
         @test all(isapprox.(sum(probs, dims=2), 1.0, atol=1e-10))
-        
+
         # Test consistency
         for i in 1:n_samples
             @test assignments[i] == argmax(probs[i, :])
         end
     end
-    
+
     @testset "Show Profiles" begin
         # Prepare data with sufficient items
         n_items = 5
         df = DataFrame(
             [Symbol("x$i") => repeat([1, 2], 50) for i in 1:n_items]...
         )
         data, n_cats = prepare_data(df, [Symbol("x$i") for i in 1:n_items]...)
-        
+
         # Fit model
         model = LCAModel(2, n_items, n_cats)
         fit!(model, data)
-        
+
         # Test basic display
         @test_nowarn show_profiles(model, df, [Symbol("x$i") for i in 1:n_items])
-        
+
         # Test with custom names
         @test_nowarn show_profiles(model, df, [Symbol("x$i") for i in 1:n_items],
-                                 var_names=["Var$i" for i in 1:n_items])
-        
+            var_names=["Var$i" for i in 1:n_items])
+
         # Test with custom labels
         custom_labels = [["No", "Yes"] for _ in 1:n_items]
         @test_nowarn show_profiles(model, df, [Symbol("x$i") for i in 1:n_items],
-                                 var_labels=custom_labels)
+            var_labels=custom_labels)
     end
 end