test_reliability.rb in statsample-0.12.0

- old
+ new

@@ -1,8 +1,6 @@
 require(File.dirname(__FILE__)+'/helpers_tests.rb')
-
-
 class StatsampleReliabilityTestCase < MiniTest::Unit::TestCase
   context Statsample::Reliability do
     context "Cronbach's alpha" do 
       setup do
         @samples=40
@@ -28,10 +26,23 @@
         total_sum=@cm.total_sum
         ind_var=@ds.fields.inject(0) {|ac,v| ac+@ds[v].variance}
         expected = @k.quo(@k-1) * (1-(ind_var.quo(total_sum)))
         assert_in_delta(expected, @a,1e-10)
       end
+      should "method cronbach_alpha_from_n_s2_cov return correct values" do
+        sa=Statsample::Reliability::ScaleAnalysis.new(@ds)
+        vm, cm = sa.variances_mean, sa.covariances_mean
+        assert_in_delta(sa.alpha, Statsample::Reliability.cronbach_alpha_from_n_s2_cov(@n_variables, vm,cm), 1e-10 )
+        
+      end
+      should "return correct n for desired alpha, covariance and variance" do
+        sa=Statsample::Reliability::ScaleAnalysis.new(@ds)
+        vm, cm = sa.variances_mean, sa.covariances_mean
+        n_obtained=Statsample::Reliability.n_for_desired_alpha(@a, vm,cm)
+        #p n_obtained
+        assert_in_delta(Statsample::Reliability.cronbach_alpha_from_n_s2_cov(n_obtained, vm,cm) ,@a,0.001) 
+      end
       should "standarized alpha will be equal to sum of matrix covariance less the individual variances on standarized values" do
         total_sum=@cme.total_sum
         ind_var=@dse.fields.inject(0) {|ac,v| ac+@dse[v].variance}
         expected = @k.quo(@k-1) * (1-(ind_var.quo(total_sum)))
         assert_in_delta(expected, @as, 1e-10)
@@ -85,17 +96,85 @@
         assert_equal(expected, @icc.curve_field('a',index))
         
       end
       
     end
-    context Statsample::Reliability::ItemAnalysis do
+    
+    context Statsample::Reliability::MultiScaleAnalysis do
+      
+      setup do
+        
+        size=100
+        @scales=4
+        @items_per_scale=10
+        h={}
+        @scales.times {|s|
+          @items_per_scale.times {|i|
+            h["#{s}_#{i}"] = (size.times.map {(s*2)+rand}).to_scale
+          }
+        }
+        @ds=h.to_dataset
+        @msa=Statsample::Reliability::MultiScaleAnalysis.new(:name=>'Multiple Analysis') do |m|
+          m.scale "complete", @ds
+          @scales.times {|s|
+            m.scale "scale_#{s}", @ds.clone(@items_per_scale.times.map {|i| "#{s}_#{i}"}), {:name=>"Scale #{s}"}
+          }
+        end
+      end
+        should "Retrieve correct ScaleAnalysis for whole scale" do
+          sa=Statsample::Reliability::ScaleAnalysis.new(@ds, :name=>"Scale complete") 
+          assert_equal(sa.variances_mean, @msa.scale("complete").variances_mean)
+        end
+        should "Retrieve correct ScaleAnalysis for each scale" do
+          @scales.times {|s|
+            sa=Statsample::Reliability::ScaleAnalysis.new(@ds.dup(@items_per_scale.times.map {|i| "#{s}_#{i}"}), :name=>"Scale #{s}")
+            assert_equal(sa.variances_mean,@msa.scale("scale_#{s}").variances_mean)
+          }
+        end
+        should "retrieve correct correlation matrix for each scale" do
+          vectors={'complete'=>@ds.vector_sum}
+          
+          @scales.times {|s|
+           vectors["scale_#{s}"]=@ds.dup(@items_per_scale.times.map {|i| "#{s}_#{i}"}).vector_sum 
+          }
+          ds2=vectors.to_dataset
+          assert_equal(Statsample::Bivariate.correlation_matrix(ds2), @msa.correlation_matrix)
+        end
+        should "delete scale using delete_scale" do
+          @msa.delete_scale("complete")
+          assert_equal(@msa.scales.keys.sort, @scales.times.map {|s| "scale_#{s}"})
+        end
+        should "retrieve pca for scales" do
+          @msa.delete_scale("complete")
+          vectors=Hash.new
+          @scales.times {|s|
+           vectors["scale_#{s}"]=@ds.dup(@items_per_scale.times.map {|i| "#{s}_#{i}"}).vector_sum 
+          }
+          ds2=vectors.to_dataset
+          cor_matrix=Statsample::Bivariate.correlation_matrix(ds2)
+          m=3
+          pca=Statsample::Factor::PCA.new(cor_matrix, :m=>m)
+          assert_equal(pca.component_matrix, @msa.pca(:m=>m).component_matrix)
+        end
+        should "retrieve acceptable summary" do
+          @msa.delete_scale("scale_0")
+          @msa.delete_scale("scale_1")
+          @msa.delete_scale("scale_2")
+          
+          
+          #@msa.summary_correlation_matrix=true
+          #@msa.summary_pca=true
+          assert(@msa.summary.size>0)
+        end
+    end
+    context Statsample::Reliability::ScaleAnalysis do
       setup do 
-        @x1=[1,1,1,1,2,2,2,2,3,3,3,30].to_vector(:scale)
-        @x2=[1,1,1,2,2,3,3,3,3,4,4,50].to_vector(:scale)
-        @x3=[2,2,1,1,1,2,2,2,3,4,5,40].to_vector(:scale)
-        @x4=[1,2,3,4,4,4,4,3,4,4,5,30].to_vector(:scale)
+        @x1=[1,1,1,1,2,2,2,2,3,3,3,30].to_scale
+        @x2=[1,1,1,2,2,3,3,3,3,4,4,50].to_scale
+        @x3=[2,2,1,1,1,2,2,2,3,4,5,40].to_scale
+        @x4=[1,2,3,4,4,4,4,3,4,4,5,30].to_scale
         @ds={'x1'=>@x1,'x2'=>@x2,'x3'=>@x3,'x4'=>@x4}.to_dataset
-        @ia=Statsample::Reliability::ItemAnalysis.new(@ds)
+        @ia=Statsample::Reliability::ScaleAnalysis.new(@ds)
         @cov_matrix=Statsample::Bivariate.covariance_matrix(@ds)
       end     
       should "return correct values for item analysis" do 
         assert_in_delta(0.980,@ia.alpha,0.001)
         assert_in_delta(0.999,@ia.alpha_standarized,0.001)