Current Versus Past DW-NOMINATE Scores

Updated 4 April 2008


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, and HL01109A21_PRES.DAT for the House; SL01105C.DAT, SL01106D.DAT, SL01107A1.DAT, SL01108A1.DAT, and SL01109B21.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, 36,177 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 7 File, 36,177 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 36,177 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 8 File, 8,746 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 7 File, 8,746 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 8,746 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 .999 and .998, 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 .948 for the first dimension and .894 for the second. The corresponding r-squares for the Senate are .933 and .904, 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 =   35739
-------------+------------------------------           F(  1, 35737) =       .
       Model |  5293.98409     1  5293.98409           Prob > F      =  0.0000
    Residual |  4.92909738 35737  .000137927           R-squared     =  0.9991
-------------+------------------------------           Adj R-squared =  0.9991
       Total |  5298.91319 35738  .148271117           Root MSE      =  .01174

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |   .9887853   .0001596  6195.36   0.000     .9884725    .9890982
       _cons |  -.0015506   .0000622   -24.93   0.000    -.0016725   -.0014287
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_109

      Source |       SS       df       MS              Number of obs =   35739
-------------+------------------------------           F(  1, 35737) =       .
       Model |  9070.91557     1  9070.91557           Prob > F      =  0.0000
    Residual |  25.0061005 35737  .000699726           R-squared     =  0.9973
-------------+------------------------------           Adj R-squared =  0.9973
       Total |  9095.92167 35738  .254516808           Root MSE      =  .02645

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9875461   .0002743  3600.49   0.000     .9870085    .9880837
       _cons |    .002047     .00014    14.62   0.000     .0017725    .0023214
------------------------------------------------------------------------------

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 =   35300
-------------+------------------------------           F(  1, 35298) =       .
       Model |  5174.85364     1  5174.85364           Prob > F      =  0.0000
    Residual |  12.4896386 35298  .000353834           R-squared     =  0.9976
-------------+------------------------------           Adj R-squared =  0.9976
       Total |  5187.34328 35299  .146954397           Root MSE      =  .01881

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9737292   .0002546  3824.28   0.000     .9732301    .9742282
       _cons |  -.0016113   .0001002   -16.08   0.000    -.0018078   -.0014149
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_108

      Source |       SS       df       MS              Number of obs =   35300
-------------+------------------------------           F(  1, 35298) =       .
       Model |  9000.41636     1  9000.41636           Prob > F      =  0.0000
    Residual |  35.7664665 35298  .001013272           R-squared     =  0.9960
-------------+------------------------------           Adj R-squared =  0.9960
       Total |  9036.18282 35299  .255989768           Root MSE      =  .03183

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |     .97049   .0003256  2980.36   0.000     .9698518    .9711283
       _cons |   .0047214   .0001695    27.85   0.000     .0043891    .0050536
------------------------------------------------------------------------------

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 =   34859
-------------+------------------------------           F(  1, 34857) =       .
       Model |  5046.28388     1  5046.28388           Prob > F      =  0.0000
    Residual |  36.1590371 34857  .001037354           R-squared     =  0.9929
-------------+------------------------------           Adj R-squared =  0.9929
       Total |  5082.44292 34858  .145804203           Root MSE      =  .03221

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .9413245   .0004268  2205.58   0.000     .9404879     .942161
       _cons |  -.0003104   .0001727    -1.80   0.072    -.0006488     .000028
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_107

      Source |       SS       df       MS              Number of obs =   34859
-------------+------------------------------           F(  1, 34857) =       .
       Model |  8848.88774     1  8848.88774           Prob > F      =  0.0000
    Residual |    127.3579 34857  .003653725           R-squared     =  0.9858
-------------+------------------------------           Adj R-squared =  0.9858
       Total |  8976.24564 34858  .257508912           Root MSE      =  .06045

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .9621936   .0006183  1556.24   0.000     .9609817    .9634054
       _cons |   .0066013   .0003239    20.38   0.000     .0059664    .0072362
------------------------------------------------------------------------------

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 =   34417
-------------+------------------------------           F(  1, 34415) =       .
       Model |  4905.26975     1  4905.26975           Prob > F      =  0.0000
    Residual |  78.8835188 34415  .002292126           R-squared     =  0.9842
-------------+------------------------------           Adj R-squared =  0.9842
       Total |  4984.15326 34416  .144820818           Root MSE      =  .04788

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .9217751   .0006301  1462.89   0.000     .9205401    .9230101
       _cons |   .0008349   .0002583     3.23   0.001     .0003287    .0013411
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_106

      Source |       SS       df       MS              Number of obs =   34417
-------------+------------------------------           F(  1, 34415) =       .
       Model |  8645.31302     1  8645.31302           Prob > F      =  0.0000
    Residual |   265.18123 34415  .007705397           R-squared     =  0.9702
-------------+------------------------------           Adj R-squared =  0.9702
       Total |  8910.49425 34416  .258905574           Root MSE      =  .08778

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .9165769   .0008653  1059.24   0.000     .9148808     .918273
       _cons |   .0122293   .0004733    25.84   0.000     .0113016    .0131569
------------------------------------------------------------------------------

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 =   33977
-------------+------------------------------           F(  1, 33975) =       .
       Model |    4638.491     1    4638.491           Prob > F      =  0.0000
    Residual |  255.539719 33975  .007521405           R-squared     =  0.9478
-------------+------------------------------           Adj R-squared =  0.9478
       Total |  4894.03072 33976  .144043758           Root MSE      =  .08673

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   1.001759   .0012756   785.31   0.000     .9992588    1.004259
       _cons |  -.0000325   .0004709    -0.07   0.945    -.0009554    .0008905
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_105

      Source |       SS       df       MS              Number of obs =   33977
-------------+------------------------------           F(  1, 33975) =       .
       Model |  7906.39612     1  7906.39612           Prob > F      =  0.0000
    Residual |  933.617206 33975  .027479535           R-squared     =  0.8944
-------------+------------------------------           Adj R-squared =  0.8944
       Total |  8840.01332 33976  .260184051           Root MSE      =  .16577

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .9202791   .0017157   536.39   0.000     .9169163    .9236419
       _cons |   .0179515   .0008994    19.96   0.000     .0161886    .0197143
------------------------------------------------------------------------------

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 |  1344.02806     1  1344.02806           Prob > F      =  0.0000
    Residual |  1.89297179  8642  .000219043           R-squared     =  0.9986
-------------+------------------------------           Adj R-squared =  0.9986
       Total |  1345.92103  8643  .155723826           Root MSE      =   .0148

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |   .9872714   .0003986  2477.08   0.000     .9864901    .9880527
       _cons |  -.0025939   .0001592   -16.29   0.000     -.002906   -.0022818
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_109

      Source |       SS       df       MS              Number of obs =    8644
-------------+------------------------------           F(  1,  8642) =       .
       Model |  2446.58379     1  2446.58379           Prob > F      =  0.0000
    Residual |   4.8294209  8642  .000558831           R-squared     =  0.9980
-------------+------------------------------           Adj R-squared =  0.9980
       Total |  2451.41321  8643  .283629899           Root MSE      =  .02364

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9767474   .0004668  2092.38   0.000     .9758323    .9776624
       _cons |  -.0017257   .0002545    -6.78   0.000    -.0022245   -.0012268
------------------------------------------------------------------------------

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 |  1313.50672     1  1313.50672           Prob > F      =  0.0000
    Residual |  10.5118164  8540  .001230892           R-squared     =  0.9921
-------------+------------------------------           Adj R-squared =  0.9921
       Total |  1324.01854  8541  .155019147           Root MSE      =  .03508

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9708836   .0009399  1033.01   0.000     .9690412    .9727259
       _cons |  -.0045223   .0003796   -11.91   0.000    -.0052665   -.0037781
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_108

      Source |       SS       df       MS              Number of obs =    8542
-------------+------------------------------           F(  1,  8540) =       .
       Model |  2410.33922     1  2410.33922           Prob > F      =  0.0000
    Residual |  24.1712731  8540   .00283036           R-squared     =  0.9901
-------------+------------------------------           Adj R-squared =  0.9901
       Total |  2434.51049  8541   .28503811           Root MSE      =   .0532

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .9447639   .0010238   922.82   0.000      .942757    .9467707
       _cons |  -.0014085   .0005761    -2.44   0.015    -.0025378   -.0002791
------------------------------------------------------------------------------

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 |  1284.11775     1  1284.11775           Prob > F      =  0.0000
    Residual |  21.0199788  8439  .002490814           R-squared     =  0.9839
-------------+------------------------------           Adj R-squared =  0.9839
       Total |  1305.13773  8440  .154637171           Root MSE      =  .04991

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .9524841   .0013266   718.01   0.000     .9498837    .9550845
       _cons |  -.0061236   .0005432   -11.27   0.000    -.0071885   -.0050587
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_107

      Source |       SS       df       MS              Number of obs =    8441
-------------+------------------------------           F(  1,  8439) =       .
       Model |  2358.97025     1  2358.97025           Prob > F      =  0.0000
    Residual |  56.9215437  8439  .006745058           R-squared     =  0.9764
-------------+------------------------------           Adj R-squared =  0.9764
       Total |  2415.89179  8440  .286243103           Root MSE      =  .08213

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .9173492   .0015512   591.38   0.000     .9143085      .92039
       _cons |   .0010107   .0008949     1.13   0.259    -.0007435    .0027649
------------------------------------------------------------------------------

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 |  1252.34478     1  1252.34478           Prob > F      =  0.0000
    Residual |  33.0613345  8337  .003965615           R-squared     =  0.9743
-------------+------------------------------           Adj R-squared =  0.9743
       Total |  1285.40611  8338  .154162402           Root MSE      =  .06297

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .9527667   .0016954   561.96   0.000     .9494432    .9560902
       _cons |  -.0042454   .0006897    -6.16   0.000    -.0055973   -.0028934
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_106

      Source |       SS       df       MS              Number of obs =    8339
-------------+------------------------------           F(  1,  8337) =       .
       Model |  2309.77911     1  2309.77911           Prob > F      =  0.0000
    Residual |  87.1617031  8337  .010454804           R-squared     =  0.9636
-------------+------------------------------           Adj R-squared =  0.9636
       Total |  2396.94081  8338  .287471913           Root MSE      =  .10225

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .8712982   .0018537   470.03   0.000     .8676645    .8749319
       _cons |   .0031537   .0011211     2.81   0.005      .000956    .0053513
------------------------------------------------------------------------------

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 |  1181.92211     1  1181.92211           Prob > F      =  0.0000
    Residual |  84.7414038  8234  .010291645           R-squared     =  0.9331
-------------+------------------------------           Adj R-squared =  0.9331
       Total |  1266.66351  8235  .153814634           Root MSE      =  .10145

------------------------------------------------------------------------------
  dwnom1_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .9630757   .0028419   338.88   0.000     .9575049    .9686466
       _cons |  -.0084485   .0011179    -7.56   0.000    -.0106398   -.0062572
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_110 dwnom2_105

      Source |       SS       df       MS              Number of obs =    8236
-------------+------------------------------           F(  1,  8234) =77737.32
       Model |   2150.3867     1   2150.3867           Prob > F      =  0.0000
    Residual |  227.770713  8234  .027662219           R-squared     =  0.9042
-------------+------------------------------           Adj R-squared =  0.9042
       Total |  2378.15742  8235  .288786572           Root MSE      =  .16632

------------------------------------------------------------------------------
  dwnom2_110 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .8788631   .0031521   278.81   0.000     .8726841    .8850421
       _cons |  -.0008779   .0018343    -0.48   0.632    -.0044735    .0027178
------------------------------------------------------------------------------



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 |  -0.0844   1.0000 
             |   0.0000
             |
  dwnom1_109 |   0.9995  -0.0871   1.0000 
             |   0.0000   0.0000
             |
  dwnom2_109 |  -0.0846   0.9986  -0.0865   1.0000 
             |   0.0000   0.0000   0.0000
             |
  dwnom1_108 |   0.9988  -0.0896   0.9995  -0.0886   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_108 |  -0.0811   0.9980  -0.0831   0.9984  -0.0851   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_107 |   0.9964  -0.0987   0.9970  -0.0974   0.9973  -0.0948   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_107 |  -0.0632   0.9929  -0.0653   0.9937  -0.0674   0.9942  -0.0755   1.0000
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_106 |   0.9921  -0.1048   0.9926  -0.1035   0.9928  -0.1015   0.9983  -0.0811   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_106 |  -0.0444   0.9850  -0.0465   0.9863  -0.0485   0.9876  -0.0564   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 |   0.9735  -0.1039   0.9744  -0.1024   0.9753  -0.1006   0.9843  -0.0799   0.9895  -0.0603   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_105 |  -0.0005   0.9457  -0.0023   0.9474  -0.0041   0.9494  -0.0098   0.9649  -0.0128   0.9734  -0.0098   1.0000 
             |   0.9333   0.0000   0.6742   0.0000   0.4487   0.0000   0.0721   0.0000   0.0186   0.0000   0.0720
             |



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 |  -0.0395   1.0000 
             |   0.0002
             |
  dwnom1_109 |   0.9993  -0.0382   1.0000 
             |   0.0000   0.0004
             |
  dwnom2_109 |  -0.0420   0.9990  -0.0423   1.0000 
             |   0.0001   0.0000   0.0001
             |
  dwnom1_108 |   0.9960  -0.0370   0.9981  -0.0412   1.0000 
             |   0.0000   0.0006   0.0000   0.0001
             |
  dwnom2_108 |  -0.0457   0.9950  -0.0461   0.9975  -0.0459   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_107 |   0.9919  -0.0298   0.9944  -0.0348   0.9966  -0.0406   1.0000 
             |   0.0000   0.0061   0.0000   0.0014   0.0000   0.0002
             |
  dwnom2_107 |  -0.0383   0.9881  -0.0377   0.9910  -0.0363   0.9943  -0.0320   1.0000
             |   0.0004   0.0000   0.0005   0.0000   0.0009   0.0000   0.0033
             |
  dwnom1_106 |   0.9871  -0.0189   0.9891  -0.0245   0.9899  -0.0317   0.9971  -0.0229   1.0000  
             |   0.0000   0.0843   0.0000   0.0254   0.0000   0.0038   0.0000   0.0369
             |
  dwnom2_106 |  -0.0375   0.9816  -0.0364   0.9845  -0.0344   0.9879  -0.0300   0.9962  -0.0231   1.0000  
             |   0.0006   0.0000   0.0009   0.0000   0.0017   0.0000   0.0061   0.0000   0.0352
             |
  dwnom1_105 |   0.9660  -0.0286   0.9685  -0.0346   0.9705  -0.0428   0.9836  -0.0331   0.9898  -0.0335   1.0000 
             |   0.0000   0.0095   0.0000   0.0017   0.0000   0.0001   0.0000   0.0027   0.0000   0.0023
             |
  dwnom2_105 |  -0.0164   0.9509  -0.0144   0.9534  -0.0111   0.9563  -0.0058   0.9725   0.0017   0.9764  -0.0085   1.0000 
             |   0.1378   0.0000   0.1925   0.0000   0.3133   0.0000   0.6018   0.0000   0.8802   0.0000   0.4410
             |