test_factor.rb in statsample-0.14.1

- old
+ new

@@ -1,13 +1,72 @@
 require(File.dirname(__FILE__)+'/helpers_tests.rb')
 
 class StatsampleFactorTestCase < MiniTest::Unit::TestCase
+  include Statsample::Fixtures
+  def test_antiimage
+    cor=Matrix[[1,0.964, 0.312],[0.964,1,0.411],[0.312,0.411,1]]
+    expected=Matrix[[0.062,-0.057, 0.074],[-0.057, 0.057, -0.089], [0.074, -0.089, 0.729]]
+    ai=Statsample::Factor.anti_image_covariance_matrix(cor)
+    assert(Matrix.equal_in_delta?(expected, ai, 0.01), "#{expected.to_s} not equal to #{ai.to_s}")
+  end
+  def test_kmo
+      @v1=[1 ,2 ,3 ,4 ,7 ,8 ,9 ,10,14,15,20,50,60,70].to_scale
+      @v2=[5 ,6 ,11,12,13,16,17,18,19,20,30,0,0,0].to_scale
+      @v3=[10,3 ,20,30,40,50,80,10,20,30,40,2,3,4].to_scale
+      # KMO: 0.490
+      ds={'v1'=>@v1,'v2'=>@v2,'v3'=>@v3}.to_dataset
+      cor=Statsample::Bivariate.correlation_matrix(ds)
+     kmo=Statsample::Factor.kmo(cor)
+     assert_in_delta(0.667, kmo,0.001)
+     assert_in_delta(0.81, Statsample::Factor.kmo(harman_817),0.01)
+     
+  end
+  def test_kmo_univariate
+    m=harman_817
+    expected=[0.73,0.76,0.84,0.87,0.53,0.93,0.78,0.86]
+    m.row_size.times.map {|i|
+      assert_in_delta(expected[i], Statsample::Factor.kmo_univariate(m,i),0.01)
+    }
+  end
+  def test_parallelanalysis_with_data
+    samples=100
+    variables=10
+    iterations=50
+    rng = GSL::Rng.alloc()
+    f1=samples.times.collect {rng.ugaussian()}.to_scale
+    f2=samples.times.collect {rng.ugaussian()}.to_scale    
+    vectors={}
+    variables.times do |i|
+      if i<5
+        vectors["v#{i}"]=samples.times.collect {|nv|
+          f1[nv]*5+f2[nv]*2+rng.ugaussian()
+        }.to_scale
+      else
+        vectors["v#{i}"]=samples.times.collect {|nv|
+          f2[nv]*5+f1[nv]*2+rng.ugaussian()
+        }.to_scale
+      end
+      
+    end
+    ds=vectors.to_dataset
+    
+    pa1=Statsample::Factor::ParallelAnalysis.new(ds, :bootstrap_method=>:data, :iterations=>iterations)
+    pa2=Statsample::Factor::ParallelAnalysis.with_random_data(samples,variables,:iterations=>iterations,:percentil=>95)
+    3.times do |n|
+      var="ev_0000#{n+1}"
+      assert_in_delta(pa1.ds_eigenvalues[var].mean,pa2.ds_eigenvalues[var].mean,0.04)
+    end
+  end
   def test_parallelanalysis
-    pa=Statsample::Factor::ParallelAnalysis.with_random_data(305,8,150)
+    pa=Statsample::Factor::ParallelAnalysis.with_random_data(305,8,:iterations=>100,:percentil=>95)
     assert_in_delta(1.2454, pa.ds_eigenvalues['ev_00001'].mean, 0.01)
     assert_in_delta(1.1542, pa.ds_eigenvalues['ev_00002'].mean, 0.01)
     assert_in_delta(1.0836, pa.ds_eigenvalues['ev_00003'].mean, 0.01)
+    #puts pa.summary
+    assert(pa.summary.size>0)
+    #pa=Statsample::Factor::ParallelAnalysis.with_random_data(305,8,100, 95, true)
+    #puts pa.summary
   end
   def test_map
     fields=%w{height arm.span forearm lower.leg weight bitro.diameter chest.girth chest.width}
     m=Matrix[ 
           [ 1, 0.846, 0.805, 0.859, 0.473, 0.398, 0.301, 0.382],
@@ -67,11 +126,13 @@
 
   # Tested with R
   def test_principalaxis
       matrix=::Matrix[
       [1.0, 0.709501601093587, 0.877596585880047, 0.272219316266807],  [0.709501601093587, 1.0, 0.291633797330304, 0.871141831433844], [0.877596585880047, 0.291633797330304, 1.0, -0.213373722977167], [0.272219316266807, 0.871141831433844, -0.213373722977167, 1.0]]
-      fa=Statsample::Factor::PrincipalAxis.new(matrix,:m=>1, :max_iterations=>50)
       
+      
+      fa=Statsample::Factor::PrincipalAxis.new(matrix,:m=>1, :max_iterations=>50)
+
       cm=::Matrix[[0.923],[0.912],[0.507],[0.483]]
       
       _test_matrix(cm,fa.component_matrix)
       
       h2=[0.852,0.832,0.257,0.233]