diff --git a/h2o-algos/src/main/java/hex/schemas/HGLMModelV3.java b/h2o-algos/src/main/java/hex/schemas/HGLMModelV3.java
index e503e12f1f31..469494742a3e 100644
--- a/h2o-algos/src/main/java/hex/schemas/HGLMModelV3.java
+++ b/h2o-algos/src/main/java/hex/schemas/HGLMModelV3.java
@@ -45,18 +45,18 @@ public static final class HGLMModelOutputV3 extends ModelOutputSchemaV3<HGLMMode
     
     @API(help="Covariance Matrix for Random Effects (= Tj in section II.I of the doc")
     public double[][] tmat;
-
-    @API(help="ratio of each random effect variance and (sum of all random effect variances plus the residual noise" +
+    
+    @API(help="Ratio of each random effect variance and (sum of all random effect variances plus the residual noise" +
             " variance).")
     double[] icc;
     
-    @API(help="residual noise variance")
+    @API(help="Residual noise variance")
     double residual_variance;
     
-    @API(help="mean residual error with fixed effect coefficients only")
+    @API(help="Mean residual error with fixed effect coefficients only")
     double mean_residual_fixed;
 
-    @API(help="mean residual error with fixed effect coefficients only")
+    @API(help="Mean residual error with fixed effect coefficients only")
     double mean_residual_fixed_valid;
     
     @Override
diff --git a/h2o-algos/src/test/java/hex/hglm/HGLMBasicTest.java b/h2o-algos/src/test/java/hex/hglm/HGLMBasicTest.java
index 8a659ba5a4e4..cf6839c3c7e5 100644
--- a/h2o-algos/src/test/java/hex/hglm/HGLMBasicTest.java
+++ b/h2o-algos/src/test/java/hex/hglm/HGLMBasicTest.java
@@ -115,7 +115,11 @@ public void testLevel2enum1() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum1, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum2", "enum3", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum2", "enum4", "enum6"}, new String[]{"num1", "num3"});
     } finally {
@@ -138,7 +142,11 @@ public void testLevel2enum1NoIntercept() {
       params._random_intercept = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum1, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum2", "enum3", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum2", "enum4", "enum6"}, new String[]{"num1", "num3"});
     } finally {
@@ -160,7 +168,11 @@ public void testLevel2enum1V2() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum1, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum2", "enum3", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum2", "enum4", "enum6"}, new String[]{"num1", "num3"});
     } finally {
@@ -183,7 +195,11 @@ public void testLevel2enum2() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum2, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum3", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"}, 
               new String[]{"enum1", "enum4", "enum6"}, new String[]{"num2", "num3"});
     } finally {
@@ -205,7 +221,11 @@ public void testLevel2enum2V2() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum2, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum3", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum4", "enum6"}, new String[]{"num2", "num3"});
     } finally {
@@ -228,7 +248,11 @@ public void testLevel2enum2V2NoRandomIntercept() {
       params._random_intercept = false;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum2, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum3", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum4", "enum6"}, new String[]{"num2", "num3"});
     } finally {
@@ -251,7 +275,11 @@ public void testLevel2enum3() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum3, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum4"}, new String[]{"num1", "num2"});
     } finally {
@@ -273,7 +301,11 @@ public void testLevel2enum3V2() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum3, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum4", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum4"}, new String[]{"num1", "num2"});
     } finally {
@@ -296,7 +328,11 @@ public void testLevel2enum4() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum4, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum3", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"}, 
               new String[]{"enum1", "enum2", "enum3", "enum6"}, new String[]{"num1", "num2"});
     } finally {
@@ -318,7 +354,11 @@ public void testLevel2enum4V2() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum4, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum3", "enum5", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum2", "enum3", "enum6"}, new String[]{"num1", "num2"});
     } finally {
@@ -340,7 +380,11 @@ public void testLevel2enum5() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum5, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum3", "enum4", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum2", "enum3", "enum6"}, new String[]{"num1", "num3"});
     } finally {
@@ -362,7 +406,11 @@ public void testLevel2enum5V2() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum5, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum3", "enum4", "enum6"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum2", "enum3", "enum6"}, new String[]{"num1", "num3"});
     } finally {
@@ -384,7 +432,11 @@ public void testLevel2enum6() {
       params._max_iterations = 0;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column, 
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum6, params._group_column, 
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum3", "enum4", "enum5"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum2", "enum3", "enum5"}, new String[]{"num1", "num3"});
     } finally {
@@ -406,7 +458,11 @@ public void testLevel2enum6V2() {
       params._showFixedMatVecs = true;
       HGLMModel model = new HGLM(params).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
       checkWithManualResults1(model, params._response_column, _simpleFrame1, params._group_column,
+=======
+      checkWithManualResults1(model, params._response_column, _simpleFrame1, _simpleFrameSortEnum6, params._group_column,
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
               new String[]{"enum1", "enum2", "enum3", "enum4", "enum5"}, new String[]{"num1", "num2", "num3"},
               new String[]{"enum1", "enum2", "enum3", "enum5"}, new String[]{"num1", "num3"});
     } finally {
@@ -415,22 +471,40 @@ public void testLevel2enum6V2() {
   }
   
   // todo: add check _yMinusXTimesZ and _yMinusfixPredSquare
+<<<<<<< HEAD
   public void checkWithManualResults1(HGLMModel model, String response, Frame fr, String level2Name, 
                                       String[] enumFixed, String[] numFixed, String[] enumRandom, String[] numRandom) {
     double[] fixedRowValues;
     double[] randomRowValues;
+=======
+  public void checkWithManualResults1(HGLMModel model, String response, Frame fr, Frame frSorted, String level2Name, 
+                                      String[] enumFixed, String[] numFixed, String[] enumRandom, String[] numRandom) {
+    double[] fixedRowValues;
+    double[] randomRowValues;
+    double[] randomRowValuesS;
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
     int numLevel2Vals = fr.vec(level2Name).domain().length;
     double[][][] afjTAfj = new double[numLevel2Vals][][];
     double[][][] arjTArj = new double[numLevel2Vals][][];
     double[][][] afjTArj = new double[numLevel2Vals][][];
     double[][] afjTYj = new double[numLevel2Vals][];
     double[][] arjTYj = new double[numLevel2Vals][];
+<<<<<<< HEAD
+=======
+    //double[][] zTTimesZ = new double[model._output._zttimesz.length][model._output._zttimesz.length];
+    double[][] tempZTTZ = new double[model._output._arjtarj[0].length][model._output._arjtarj[0].length];
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
     double yMinusfixPredSquare = 0;
     double[][] yMinusXTimesZ = new double[numLevel2Vals][model._output._yMinusXTimesZ[0].length];
     double[] beta = model._output._beta;
 
     int numRow = (int) fr.numRows();
+<<<<<<< HEAD
     double responseVal;
+=======
+    double responseVal; 
+    int unit2LevelS;
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
     int unit2Level;
     double fixEffect;
     double respMinusFix;
@@ -438,7 +512,13 @@ public void checkWithManualResults1(HGLMModel model, String response, Frame fr,
     for (int rowInd = 0; rowInd < numRow; rowInd++) {
       fixedRowValues = grabRow2Arrays(enumFixed, numFixed, true, rowInd, fr, model._parms._use_all_factor_levels);
       randomRowValues = grabRow2Arrays(enumRandom, numRandom, model._parms._random_intercept, rowInd, fr, model._parms._use_all_factor_levels);
+<<<<<<< HEAD
+      responseVal = fr.vec(response).at(rowInd);
+=======
+      randomRowValuesS = grabRow2Arrays(enumRandom, numRandom, model._parms._random_intercept, rowInd, frSorted, model._parms._use_all_factor_levels);
       responseVal = fr.vec(response).at(rowInd);
+      unit2LevelS = (int) frSorted.vec(level2Name).at(rowInd);
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
       unit2Level = (int) fr.vec(level2Name).at(rowInd);
       // calculate the various matrices and vectors
       formMatrix(afjTAfj, unit2Level, fixedRowValues, fixedRowValues); // calculate afjTAfj
@@ -446,6 +526,10 @@ public void checkWithManualResults1(HGLMModel model, String response, Frame fr,
       formMatrix(afjTArj, unit2Level, fixedRowValues, randomRowValues); // calculate afjTArj
       formVector(afjTYj, unit2Level, fixedRowValues, responseVal); // calculate afjTYj
       formVector(arjTYj, unit2Level, randomRowValues, responseVal); // calculate arjTYj
+<<<<<<< HEAD
+=======
+  //    formZTTimesZ(zTTimesZ, unit2LevelS, randomRowValuesS, tempZTTZ);
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
       fixEffect = innerProduct(fixedRowValues, beta);
       respMinusFix = responseVal - fixEffect;
       yMinusfixPredSquare += respMinusFix*respMinusFix;
@@ -458,6 +542,10 @@ public void checkWithManualResults1(HGLMModel model, String response, Frame fr,
     check3DArrays(model._output._afjtafj, afjTAfj, TOL);
     check3DArrays(model._output._afjtarj, afjTArj, TOL);
     check3DArrays(model._output._arjtarj, arjTArj, TOL);
+<<<<<<< HEAD
+=======
+  //  checkDoubleArrays(model._output._zttimesz, zTTimesZ, TOL);
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
     checkDoubleArrays(model._output._yMinusXTimesZ, yMinusXTimesZ, TOL);
     assertEquals(model._output._yMinusFixPredSquare, yMinusfixPredSquare, TOL);
   }
@@ -683,6 +771,7 @@ public static Frame makeUBetaFrame(double[][] initUBeta) {
   
   @Test
   public void testRandomInterceptOnly() {
+<<<<<<< HEAD
       Scope.enter();
       try {
         HGLMModel.HGLMParameters params = new HGLMModel.HGLMParameters();
@@ -701,6 +790,9 @@ public void testRandomInterceptOnly() {
       } finally {
         Scope.exit();
       }
+=======
+    ;
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
   }
 
   @Test
@@ -725,6 +817,13 @@ public void testSemiconductor() {
       // just make sure it runs
       HGLMModel model = new HGLM(parms).trainModel().get();
       Scope.track_generic(model);
+<<<<<<< HEAD
+=======
+      ;
+ //     ModelMetricsHGLMGaussianGaussian mmetrics = (ModelMetricsHGLMGaussianGaussian) model._output._training_metrics;
+ //     Scope.track_generic(mmetrics);
+ //     assertEquals(363.6833, mmetrics._hlik, 1e-4);
+>>>>>>> ef3bae3d71df761ab365c4a13f439adfc7987f1e
     } finally {
       Scope.exit();
     }