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st: strange test statistic with ologit


From   "Stephan Rudolfer" <stephan.rudolfer@manchester.ac.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: strange test statistic with ologit
Date   Tue, 11 Jul 2006 16:59:16 +0100

Consider the following output:

. * fit PO model +/- ulnar sensories
. ologit drdiag *N*

Iteration 0:   log likelihood = -1119.5116
Iteration 1:   log likelihood = -649.97107
Iteration 2:   log likelihood =  -515.7447
Iteration 3:   log likelihood =  -485.6584
Iteration 4:   log likelihood = -481.75898
Iteration 5:   log likelihood = -481.65813
Iteration 6:   log likelihood = -481.64452

Ordered logistic regression                       Number of obs   =        958
                                                  LR chi2(28)     =    1275.68
                                                  Prob > chi2     =     0.0000
Log likelihood = -481.66917                       Pseudo R2       =     0.5698

------------------------------------------------------------------------------
      drdiag |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   I_N_mmdew |  -.0465321   .0793397    -0.59   0.558    -.2020351    .1089708
   C_N_mmdew |  -.0203119   .0787507    -0.26   0.796    -.1746604    .1340366
    I_N_mmle |   .0514223   .0368836     1.39   0.163    -.0208682    .1237128
    C_N_mmle |  -.0034031   .0368515    -0.09   0.926    -.0756308    .0688245
    I_N_mmlw |   .3441091   .0874833     3.93   0.000      .172645    .5155731
    C_N_mmlw |  -.0034267   .0274017    -0.13   0.900    -.0571331    .0502797
   I_N_mmvew |   .0221535   .0162648     1.36   0.173    -.0097248    .0540319
   C_N_mmvew |   .0018161   .0169144     0.11   0.914    -.0313356    .0349678
     I_N_msa |  -.3507796   .0248112   -14.14   0.000    -.3994087   -.3021506
     C_N_msa |   .0426585   .0140432     3.04   0.002     .0151342    .0701827
     I_N_msd |  -.5302222     .17639    -3.01   0.003    -.8759402   -.1845042
     C_N_msd |   .0672648   .1734114     0.39   0.698    -.2726153    .4071448
     I_N_msl |   .5509079   .1805359     3.05   0.002      .197064    .9047518
     C_N_msl |  -.0659334   .1767698    -0.37   0.709    -.4123958     .280529
   I_N_umdew |      .2726   .1575625     1.73   0.084    -.0362168    .5814168
   C_N_umdew |   .2643918   .1388434     1.90   0.057    -.0077364    .5365199
    I_N_umle |  -1.694766   .7929332    -2.14   0.033    -3.248886   -.1406452
    C_N_umle |  -1.079238   .6767046    -1.59   0.111    -2.405555    .2470784
    I_N_umlw |   1.859347   .8334382     2.23   0.026     .2258385    3.492856
    C_N_umlw |    1.01231   .6405643     1.58   0.114    -.2431728    2.267793
   I_N_umvew |  -.1485646   .0739676    -2.01   0.045    -.2935385   -.0035907
   C_N_umvew |  -.1232485   .0669461    -1.84   0.066    -.2544605    .0079635
     I_N_usa |   .0467586   .0220822     2.12   0.034     .0034783    .0900389
     C_N_usa |   .0137817   .0216102     0.64   0.524    -.0285736    .0561371
     I_N_usd |   .0907833   .2644875     0.34   0.731    -.4276027    .6091693
     C_N_usd |   .3527155   .2673818     1.32   0.187    -.1713433    .8767743
     I_N_usl |  -.0666454   .2676676    -0.25   0.803    -.5912643    .4579735
     C_N_usl |  -.3652971   .2701481    -1.35   0.176    -.8947777    .1641835
-------------+----------------------------------------------------------------
       /cut1 |  -17.09747   4.765666                       -26.438   -7.756936
       /cut2 |  -12.30247   4.741235                     -21.59512   -3.009824
       /cut3 |  -7.826134   4.744828                     -17.12583    1.473559
------------------------------------------------------------------------------
Note: 8 observations completely determined.  Standard errors questionable.

. predict p1-p4,p

 
. 
. drop *N_us* p1-p4

. ologit drdiag *N*

Iteration 0:   log likelihood = -1119.5116
Iteration 1:   log likelihood = -656.43282
Iteration 2:   log likelihood = -524.31049
Iteration 3:   log likelihood = -495.86739
Iteration 4:   log likelihood = -492.94665
Iteration 5:   log likelihood = -492.73041
Iteration 6:   log likelihood = -492.63081
Iteration 7:   log likelihood = -492.60684
Iteration 8:   log likelihood = -492.59498
Iteration 9:   log likelihood = -492.59203

Ordered logistic regression                       Number of obs   =        958
                                                  F(  22,      .) =          .
                                                  Prob > F        =          .

------------------------------------------------------------------------------
      drdiag |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   I_N_mmdew |  -.1012959   .0743724    -1.36   0.173    -.2470631    .0444714
   C_N_mmdew |  -.0384633   .0749714    -0.51   0.608    -.1854046     .108478
    I_N_mmle |   .0283245   .0345582     0.82   0.412    -.0394084    .0960574
    C_N_mmle |  -.0117914   .0346038    -0.34   0.733    -.0796135    .0560307
    I_N_mmlw |   .3946387    .083318     4.74   0.000     .2313384     .557939
    C_N_mmlw |  -.0026395   .0255062    -0.10   0.918    -.0526307    .0473517
   I_N_mmvew |    .020854   .0157095     1.33   0.184     -.009936    .0516441
   C_N_mmvew |  -.0018095   .0163584    -0.11   0.912    -.0338714    .0302525
     I_N_msa |  -.3012712   .0218397   -13.79   0.000    -.3440762   -.2584662
     C_N_msa |    .051534   .0128145     4.02   0.000      .026418    .0766501
     I_N_msd |  -.4987482   .1673857    -2.98   0.003    -.8268181   -.1706783
     C_N_msd |   .0181639   .1638522     0.11   0.912    -.3029804    .3393083
     I_N_msl |   .5195525   .1712748     3.03   0.002       .18386    .8552449
     C_N_msl |  -.0149123   .1670169    -0.09   0.929    -.3422595    .3124349
   I_N_umdew |   .2762426   .1487444     1.86   0.063    -.0152912    .5677763
   C_N_umdew |   .3055238   .1279053     2.39   0.017      .054834    .5562137
    I_N_umle |  -1.458214   .7457293    -1.96   0.051    -2.919817    .0033881
    C_N_umle |  -1.256245   .6103501    -2.06   0.040     -2.45251    -.059981
    I_N_umlw |   1.558035   .7845292     1.99   0.047     .0203864    3.095684
    C_N_umlw |   1.175192   .5809772     2.02   0.043     .0364972    2.313886
   I_N_umvew |  -.1252843   .0701761    -1.79   0.074     -.262827    .0122584
   C_N_umvew |  -.1312112   .0619243    -2.12   0.034    -.2525805   -.0098419
-------------+----------------------------------------------------------------
       /cut1 |  -16.51915   4.513598                     -25.36564   -7.672665
       /cut2 |  -12.14342   4.486871                     -20.93752   -3.349314
       /cut3 |  -7.916497   4.498279                     -16.73296    .8999674
------------------------------------------------------------------------------
Note: 8 observations completely determined.  Standard errors questionable.

I am astonished that Stata has given the F-statistic instead of the chi-squared one in the second model fitted.  This seems to have occurred just because I deleted the last six variables of the first model.  Having asked two Stata experts, who told me that they had never seen anything like this, I am hoping that someone on statalist will be able to clarify the situation for me.  Many thanks in advance.


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