DW-NOMINATE Comparisons Page

Current Versus Past DW-NOMINATE Scores

Updated 2 January 2009

The STATA and Excel files below combine current legislator scores (HL01110C21_PRES.DAT for the House; SL01110C21.DAT for the Senate) with the past five releases of the DW-NOMINATE legislator scores (HL01105D.SRT, HL01106C.DAT, HL01107A1.DAT, HL01108A1_PRES.DAT, HL01109A21_PRES.DAT, and HL01110D21_PRES_NEW.DAT for the House; SL01105C.DAT, SL01106D.DAT, SL01107A1.DAT, SL01108A1.DAT, SL01109B21.DAT, and SL01110C21_NEW.DAT the Senate). The DW-NOMINATE scalings for Congresses 1 - 105 were done in late 1998 using an early version of DW-NOMINATE. Because of computer limitations, this early version (1996-98) had a clumsy design that necessitated running the legislator, roll call, and utility function parameters in separate computer programs. Each program read the results from the previous one -- DW-NOMINATE was in fact a battery of programs. Also, given that only 100 - 200mhz machines were available at the time this early version was developed meant that there had to be some tradeoffs between precision and computer time.

In 2000 we developed a much improved version of DW-NOMINATE that does not have the limitations of the original battery of programs. DW-NOMINATE is now a stand-alone program like our original D-NOMINATE Program and it runs very efficiently on current high-speed PCs. The past five releases are from this version.

Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 8 File, 36,189 lines)

Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 7 File, 36,189 lines)

Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 36,189 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 8 File, 8,747 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 7 File, 8,747 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 8,747 lines)

Below are the results of regressing the current dimensions on the corresponding dimensions of the previous releases. The r-squares for the current House with the 1 to 109 House release are .999 for the first dimension and .997 for the second. The corresponding r-squares for the Senate are .998 and .997, respectively. The regression tables give the mapping of the 1 - 109 into the current release for the House and Senate.

The r-squares for the current House with the 1 to 105 House scaling released in late 1998 are .946 for the first dimension and .893 for the second. The corresponding r-squares for the Senate are .932 and .903, respectively. These r-squares are lower for the reasons given above. The regression tables give the mapping of the 1 - 105 into the current release.

As noted on the DW-NOMINATE Scores Page, when a new Congress is added to the dataset this will slightly change the scores for more recent members because their scores are estimated using their entire voting history. This will also slightly change the overall means of the dimensions. In addition, the past few Congresses are nearly unidimensional with correct classifications of 90 percent or better. Consequently, the overall fit of the DW-NOMINATE estimation has increased as recent Congresses have been added to the dataset. Consequently, the r-squares of the 1 to 110 coordinates with previous releases decline slightly with the decline being greater the earlier the release.



House: 1 to 110 vs. 1 to 109 DW-NOMINATE Scalings


Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_109

      Source |       SS       df       MS              Number of obs =   35742
-------------+------------------------------           F(  1, 35740) =       .
       Model |  5257.14913     1  5257.14913           Prob > F      =  0.0000
    Residual |  8.06477922 35740  .000225651           R-squared     =  0.9985
-------------+------------------------------           Adj R-squared =  0.9985
       Total |  5265.21391 35741  .147315797           Root MSE      =  .01502

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |   .9852488   .0002041  4826.77   0.000     .9848487    .9856489
       _cons |   -.002254   .0000796   -28.33   0.000    -.0024099   -.0020981
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_109

      Source |       SS       df       MS              Number of obs =   35742
-------------+------------------------------           F(  1, 35740) =       .
       Model |  8998.03667     1  8998.03667           Prob > F      =  0.0000
    Residual |   30.494768 35740  .000853239           R-squared     =  0.9966
-------------+------------------------------           Adj R-squared =  0.9966
       Total |  9028.53144 35741  .252609928           Root MSE      =  .02921

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9835304   .0003029  3247.42   0.000     .9829368     .984124
       _cons |   .0028691   .0001546    18.56   0.000      .002566    .0031722
------------------------------------------------------------------------------

House: 1 to 110 vs. 1 to 108 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_108

      Source |       SS       df       MS              Number of obs =   35303
-------------+------------------------------           F(  1, 35301) =       .
       Model |  5134.62569     1  5134.62569           Prob > F      =  0.0000
    Residual |  17.8356571 35301  .000505245           R-squared     =  0.9965
-------------+------------------------------           Adj R-squared =  0.9965
       Total |  5152.46135 35302  .145953809           Root MSE      =  .02248

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9698486   .0003042  3187.89   0.000     .9692523    .9704449
       _cons |  -.0024152   .0001198   -20.17   0.000      -.00265   -.0021805
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_108

      Source |       SS       df       MS              Number of obs =   35303
-------------+------------------------------           F(  1, 35301) =       .
       Model |  8925.82118     1  8925.82118           Prob > F      =  0.0000
    Residual |  42.4913301 35301  .001203686           R-squared     =  0.9953
-------------+------------------------------           Adj R-squared =  0.9953
       Total |  8968.31251 35302  .254045451           Root MSE      =  .03469

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .9664215   .0003549  2723.12   0.000     .9657259    .9671171
       _cons |   .0054834   .0001848    29.68   0.000     .0051213    .0058456
------------------------------------------------------------------------------

House: 1 to 110 vs. 1 to 107 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_107

      Source |       SS       df       MS              Number of obs =   34862
-------------+------------------------------           F(  1, 34860) =       .
       Model |  5005.35542     1  5005.35542           Prob > F      =  0.0000
    Residual |  41.0076567 34860  .001176353           R-squared     =  0.9919
-------------+------------------------------           Adj R-squared =  0.9919
       Total |  5046.36308 34861  .144756693           Root MSE      =   .0343

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .9374177   .0004544  2062.76   0.000      .936527    .9383085
       _cons |  -.0012175   .0001839    -6.62   0.000    -.0015778   -.0008571
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_107

      Source |       SS       df       MS              Number of obs =   34862
-------------+------------------------------           F(  1, 34860) =       .
       Model |  8773.81468     1  8773.81468           Prob > F      =  0.0000
    Residual |  134.385365 34860  .003855002           R-squared     =  0.9849
-------------+------------------------------           Adj R-squared =  0.9849
       Total |  8908.20005 34861   .25553484           Root MSE      =  .06209

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .9580653   .0006351  1508.63   0.000     .9568206      .95931
       _cons |   .0073144   .0003327    21.98   0.000     .0066623    .0079666
------------------------------------------------------------------------------

House: 1 to 110 vs. 1 to 106 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_106

      Source |       SS       df       MS              Number of obs =   34420
-------------+------------------------------           F(  1, 34418) =       .
       Model |  4863.24307     1  4863.24307           Prob > F      =  0.0000
    Residual |   83.080814 34418  .002413877           R-squared     =  0.9832
-------------+------------------------------           Adj R-squared =  0.9832
       Total |  4946.32388 34419  .143709111           Root MSE      =  .04913

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .9177393   .0006466  1419.40   0.000      .916472    .9190066
       _cons |   -.000166    .000265    -0.63   0.531    -.0006854    .0003535
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_106

      Source |       SS       df       MS              Number of obs =   34420
-------------+------------------------------           F(  1, 34418) =       .
       Model |  8566.99829     1  8566.99829           Prob > F      =  0.0000
    Residual |  275.558519 34418  .008006233           R-squared     =  0.9688
-------------+------------------------------           Adj R-squared =  0.9688
       Total |  8842.55681 34419  .256909173           Root MSE      =  .08948

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .9123829    .000882  1034.43   0.000     .9106541    .9141117
       _cons |   .0128819   .0004824    26.70   0.000     .0119363    .0138274
------------------------------------------------------------------------------

House:  1 to 110 vs. 1 to 105 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_105

      Source |       SS       df       MS              Number of obs =   33980
-------------+------------------------------           F(  1, 33978) =       .
       Model |   4592.4433     1   4592.4433           Prob > F      =  0.0000
    Residual |  261.620339 33978  .007699698           R-squared     =  0.9461
-------------+------------------------------           Adj R-squared =  0.9461
       Total |  4854.06363 33979  .142854811           Root MSE      =  .08775

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .9966679   .0012905   772.30   0.000     .9941385    .9991974
       _cons |  -.0010939   .0004764    -2.30   0.022    -.0020277   -.0001601
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_105

      Source |       SS       df       MS              Number of obs =   33980
-------------+------------------------------           F(  1, 33978) =       .
       Model |  7832.51429     1  7832.51429           Prob > F      =  0.0000
    Residual |  939.831521 33978  .027660001           R-squared     =  0.8929
-------------+------------------------------           Adj R-squared =  0.8929
       Total |  8772.34581 33979  .258169629           Root MSE      =  .16631

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .9159333   .0017212   532.14   0.000     .9125597     .919307
       _cons |   .0185441   .0009023    20.55   0.000     .0167755    .0203126
------------------------------------------------------------------------------

Senate:  1 to 110 vs. 1 to 109 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_109

      Source |       SS       df       MS              Number of obs =    8644
-------------+------------------------------           F(  1,  8642) =       .
       Model |  1339.01815     1  1339.01815           Prob > F      =  0.0000
    Residual |  2.49565757  8642  .000288782           R-squared     =  0.9981
-------------+------------------------------           Adj R-squared =  0.9981
       Total |  1341.51381  8643  .155213908           Root MSE      =  .01699

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |   .9854296   .0004576  2153.32   0.000     .9845326    .9863267
       _cons |  -.0036397   .0001828   -19.91   0.000     -.003998   -.0032813
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_109

      Source |       SS       df       MS              Number of obs =    8644
-------------+------------------------------           F(  1,  8642) =       .
       Model |  2426.25276     1  2426.25276           Prob > F      =  0.0000
    Residual |  6.39452043  8642  .000739935           R-squared     =  0.9974
-------------+------------------------------           Adj R-squared =  0.9974
       Total |  2432.64728  8643   .28145867           Root MSE      =   .0272

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9726805   .0005372  1810.80   0.000     .9716276    .9737335
       _cons |   -.002339   .0002928    -7.99   0.000     -.002913   -.0017649
------------------------------------------------------------------------------

Senate:  1 to 110 vs. 1 to 108 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_108

      Source |       SS       df       MS              Number of obs =    8542
-------------+------------------------------           F(  1,  8540) =       .
       Model |  1307.82582     1  1307.82582           Prob > F      =  0.0000
    Residual |  11.7890822  8540  .001380455           R-squared     =  0.9911
-------------+------------------------------           Adj R-squared =  0.9911
       Total |   1319.6149  8541  .154503559           Root MSE      =  .03715

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9687818   .0009953   973.34   0.000     .9668307    .9707328
       _cons |  -.0055402    .000402   -13.78   0.000    -.0063283   -.0047521
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_108

      Source |       SS       df       MS              Number of obs =    8542
-------------+------------------------------           F(  1,  8540) =       .
       Model |  2388.75061     1  2388.75061           Prob > F      =  0.0000
    Residual |  27.1050263  8540  .003173891           R-squared     =  0.9888
-------------+------------------------------           Adj R-squared =  0.9888
       Total |  2415.85563  8541  .282853955           Root MSE      =  .05634

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .9405234   .0010841   867.54   0.000     .9383983    .9426486
       _cons |  -.0020017   .0006101    -3.28   0.001    -.0031976   -.0008057
------------------------------------------------------------------------------

Senate:  1 to 110 vs. 1 to 107 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_107

      Source |       SS       df       MS              Number of obs =    8441
-------------+------------------------------           F(  1,  8439) =       .
       Model |  1278.26946     1  1278.26946           Prob > F      =  0.0000
    Residual |  22.3455536  8439  .002647891           R-squared     =  0.9828
-------------+------------------------------           Adj R-squared =  0.9828
       Total |  1300.61501  8440  .154101304           Root MSE      =  .05146

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .9503126   .0013677   694.80   0.000     .9476315    .9529938
       _cons |  -.0071145   .0005601   -12.70   0.000    -.0082125   -.0060166
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_107

      Source |       SS       df       MS              Number of obs =    8441
-------------+------------------------------           F(  1,  8439) =       .
       Model |  2336.96936     1  2336.96936           Prob > F      =  0.0000
    Residual |  60.5658325  8439  .007176897           R-squared     =  0.9747
-------------+------------------------------           Adj R-squared =  0.9747
       Total |  2397.53519  8440   .28406815           Root MSE      =  .08472

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .9130614   .0016001   570.63   0.000     .9099248    .9161979
       _cons |   .0004424   .0009231     0.48   0.632     -.001367    .0022519
------------------------------------------------------------------------------

Senate:  1 to 110 vs. 1 to 106 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_106

      Source |       SS       df       MS              Number of obs =    8339
-------------+------------------------------           F(  1,  8337) =       .
       Model |  1246.62119     1  1246.62119           Prob > F      =  0.0000
    Residual |  34.0577719  8337  .004085135           R-squared     =  0.9734
-------------+------------------------------           Adj R-squared =  0.9734
       Total |  1280.67896  8338  .153595462           Root MSE      =  .06392

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |    .950587   .0017208   552.41   0.000     .9472138    .9539602
       _cons |  -.0052253      .0007    -7.46   0.000    -.0065975   -.0038531
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_106

      Source |       SS       df       MS              Number of obs =    8339
-------------+------------------------------           F(  1,  8337) =       .
       Model |   2288.5304     1   2288.5304           Prob > F      =  0.0000
    Residual |  90.3418636  8337  .010836256           R-squared     =  0.9620
-------------+------------------------------           Adj R-squared =  0.9620
       Total |  2378.87226  8338  .285304901           Root MSE      =   .1041

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .8672812   .0018872   459.56   0.000     .8635818    .8709806
       _cons |     .00259   .0011414     2.27   0.023     .0003527    .0048274
------------------------------------------------------------------------------

Senate:  1 to 110 vs. 1 to 105 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_110 dwnom1_105

      Source |       SS       df       MS              Number of obs =    8236
-------------+------------------------------           F(  1,  8234) =       .
       Model |  1176.09227     1  1176.09227           Prob > F      =  0.0000
    Residual |  85.7188101  8234  .010410349           R-squared     =  0.9321
-------------+------------------------------           Adj R-squared =  0.9321
       Total |  1261.81108  8235  .153225389           Root MSE      =  .10203

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .9606976   .0028582   336.12   0.000     .9550948    .9663005
       _cons |  -.0094043   .0011243    -8.36   0.000    -.0116082   -.0072003
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_105

      Source |       SS       df       MS              Number of obs =    8236
-------------+------------------------------           F(  1,  8234) =76396.60
       Model |  2130.70056     1  2130.70056           Prob > F      =  0.0000
    Residual |  229.646196  8234  .027889992           R-squared     =  0.9027
-------------+------------------------------           Adj R-squared =  0.9027
       Total |  2360.34676  8235  .286623772           Root MSE      =    .167

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .8748309   .0031651   276.40   0.000     .8686266    .8810353
       _cons |  -.0014092   .0018418    -0.77   0.444    -.0050197    .0022013
------------------------------------------------------------------------------


House Correlation Matrix All DW-NOMINATE Scalings


. pwcorr dwnom1_110 dwnom2_110 dwnom1_109 dwnom2_109 dwnom1_108 dwnom2_108 dwnom1_107 dwnom2_107 dwnom1_106 dwnom2_106 dwnom1_105 dwnom2_105, sig

             | dwnom1~0 dwnom2~0 dwnom1~9 dwnom2~9 dwnom1~8 dwnom2~8 dwnom1~7 dwnom2~7 dwnom1~6 dwnom2~6 dwnom1~5 dwnom2~5
-------------+------------------------------------------------------------------------------------------------------------
  dwnom1_110 |   1.0000 
             |
             |
  dwnom2_110 |  -0.0831   1.0000 
             |   0.0000
             |
  dwnom1_109 |   0.9992  -0.0867   1.0000 
             |   0.0000   0.0000
             |
  dwnom2_109 |  -0.0836   0.9983  -0.0865   1.0000 
             |   0.0000   0.0000   0.0000
             |
  dwnom1_108 |   0.9983  -0.0893   0.9995  -0.0886   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_108 |  -0.0801   0.9976  -0.0830   0.9984  -0.0850   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_107 |   0.9959  -0.0984   0.9970  -0.0973   0.9973  -0.0948   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_107 |  -0.0621   0.9924  -0.0652   0.9937  -0.0673   0.9942  -0.0754   1.0000
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_106 |   0.9916  -0.1045   0.9926  -0.1034   0.9928  -0.1014   0.9983  -0.0810   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_106 |  -0.0433   0.9843  -0.0464   0.9864  -0.0485   0.9876  -0.0563   0.9962  -0.0615   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_105 |   0.9727  -0.1037   0.9744  -0.1024   0.9753  -0.1005   0.9843  -0.0798   0.9895  -0.0602   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_105 |   0.0006   0.9449  -0.0022   0.9474  -0.0041   0.9494  -0.0097   0.9649  -0.0128   0.9734  -0.0098   1.0000 
             |   0.9063   0.0000   0.6814   0.0000   0.4545   0.0000   0.0736   0.0000   0.0186   0.0000   0.0736
             |


Senate Correlation Matrix All DW-NOMINATE Scalings


. pwcorr dwnom1_110 dwnom2_110 dwnom1_109 dwnom2_109 dwnom1_108 dwnom2_108 dwnom1_107 dwnom2_107 dwnom1_106 dwnom2_106 dwnom1_105 dwnom2_105, sig

             | dwnom1~0 dwnom2~0 dwnom1~9 dwnom2~9 dwnom1~8 dwnom2~8 dwnom1~7 dwnom2~7 dwnom1~6 dwnom2~6 dwnom1~5 dwnom2~5
-------------+------------------------------------------------------------------------------------------------------------
  dwnom1_110 |   1.0000 
             |
             |
  dwnom2_110 |  -0.0375   1.0000 
             |   0.0004
             |
  dwnom1_109 |   0.9991  -0.0365   1.0000 
             |   0.0000   0.0007
             |
  dwnom2_109 |  -0.0417   0.9987  -0.0423   1.0000 
             |   0.0001   0.0000   0.0001
             |
  dwnom1_108 |   0.9955  -0.0352   0.9981  -0.0412   1.0000 
             |   0.0000   0.0011   0.0000   0.0001
             |
  dwnom2_108 |  -0.0454   0.9944  -0.0461   0.9975  -0.0459   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_107 |   0.9914  -0.0279   0.9944  -0.0348   0.9966  -0.0406   1.0000 
             |   0.0000   0.0104   0.0000   0.0014   0.0000   0.0002
             |
  dwnom2_107 |  -0.0380   0.9873  -0.0377   0.9910  -0.0363   0.9943  -0.0320   1.0000
             |   0.0005   0.0000   0.0005   0.0000   0.0009   0.0000   0.0033
             |
  dwnom1_106 |   0.9866  -0.0169   0.9891  -0.0245   0.9899  -0.0317   0.9971  -0.0229   1.0000  
             |   0.0000   0.1232   0.0000   0.0254   0.0000   0.0038   0.0000   0.0369
             |
  dwnom2_106 |  -0.0373   0.9808  -0.0364   0.9845  -0.0344   0.9879  -0.0300   0.9962  -0.0231   1.0000  
             |   0.0007   0.0000   0.0009   0.0000   0.0017   0.0000   0.0061   0.0000   0.0352
             |
  dwnom1_105 |   0.9654  -0.0265   0.9685  -0.0346   0.9705  -0.0428   0.9836  -0.0331   0.9898  -0.0335   1.0000 
             |   0.0000   0.0161   0.0000   0.0017   0.0000   0.0001   0.0000   0.0027   0.0000   0.0023
             |
  dwnom2_105 |  -0.0161   0.9501  -0.0144   0.9534  -0.0111   0.9563  -0.0058   0.9725   0.0017   0.9764  -0.0085   1.0000 
             |   0.1430   0.0000   0.1925   0.0000   0.3133   0.0000   0.6018   0.0000   0.8802   0.0000   0.4410
             |