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st: strange test statistic with ologit
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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