Hi,
I just ran an analysis and the numbers in the table look very clumsy. They truncate and break into the next line randomly, making it difficult to easily match where a number starts, stops or continues.
I will appreciate some help on how to make numbers on tables in Stata output window more readable.
I have copied the result table below.
Thank you.
Iteration 0: log pseudolikelihood = -2
> 367.3412
Iteration 1: log pseudolikelihood = -2
> 325.3225
Iteration 2: log pseudolikelihood = -2
> 324.4821
Iteration 3: log pseudolikelihood = -
> 2324.481
Iteration 4: log pseudolikelihood = -
> 2324.481
Conditional logit choice model
> Number of obs
> = 7,668
Case ID variable: _caseid
> Number of cases
> = 2556
Alternatives variable: alt
> Alts per case: min = 3
> avg = 3.0
> max = 3
> Wald chi2(47)
> = 307.11
Log pseudolikelihood = -2324.481
> Prob > chi2
> = 0.0000
(Std. err. adjust
> ed for 213 clusters in participant_id)
----------------------------------------
> --------------------------------------
| Robust
choice_bin~y | Coefficient std. err.
> z
> P>|z|
> [95% con
> f. interval]
-------------+--------------------------
> --------------------------------------
alt |
probiotics | .5986185 .0926788
> 6.46
> 0.000
> .4169715
> .7802655
ghg | .1314119 .0247864
> 5.30
> 0.000
> .0828314
> .1799923
price | -.5302432 .0453643 -
> 11.69
> 0.000
> -.6191556
> -.4413309
-------------+--------------------------
> --------------------------------------
1 | (base alternative)
-------------+--------------------------
> --------------------------------------
2 |
treatment | .0794242 .1524742
> 0.52
> 0.602
> -.2194198
> .3782681
age | -.0040665 .0055171
> -0.74
> 0.461
> -.0148798
> .0067468
hh_children | -.1385581 .077862
> -1.78
> 0.075
> -.2911648
> .0140485
consume_co~k | .1144744 .0760616
> 1.51
> 0.132
> -.0346036
> .2635523
consume_pl~k | -.0060073 .059937
> -0.10
> 0.920
> -.1234816
> .111467
consume_yo~t | .029042 .0609137
> 0.48
> 0.634
> -.0903466
> .1484306
milk_type_~d | -.0913142 .0749532
> -1.22
> 0.223
> -.2382198
> .0555915
purchase_o~c | .673682 .1698245
> 3.97
> 0.000
> .3408321
> 1.006532
primary_sh~r | -.047291 .0974846
> -0.49
> 0.628
> -.2383572
> .1437753
lactose_in~t | .3394884 .1682233
> 2.02
> 0.044
> .0097767
> .6692001
vegan | .1019169 .5041907
> 0.20
> 0.840
> -.8862787
> 1.090112
heard_ghg | -.3259258 .303606
> -1.07
> 0.283
> -.9209825
> .2691309
heard_prob~s | 1.472352 .2881793
> 5.11
> 0.000
> .9075308
> 2.037173
probiotics~r | -.0816183 .0626449
> -1.30
> 0.193
> -.2044
> .0411635
ghg_mitiga~e | -.0281954 .0752327
> -0.37
> 0.708
> -.1756488
> .1192579
imp_health~s | .1371347 .1046537
> 1.31
> 0.190
> -.0679828
> .3422521
imp_env_im~t | .0627153 .0776906
> 0.81
> 0.420
> -.0895554
> .2149861
imp_price | -.1473768 .098069
> -1.50
> 0.133
> -.3395886
> .0448349
gender | -.1641028 .1399933
> -1.17
> 0.241
> -.4384847
> .1102791
race | .0232985 .1767956
> 0.13
> 0.895
> -.3232145
> .3698115
education | .0186446 .0613061
> 0.30
> 0.761
> -.1015131
> .1388024
hh_income | -.001493 .0422218
> -0.04
> 0.972
> -.0842462
> .0812602
_cons | -1.19653 1.024643
> -1.17
> 0.243
> -3.204793
> .8117321
-------------+--------------------------
> --------------------------------------
3 |
treatment | -.1707717 .2764224
> -0.62
> 0.537
> -.7125497
> .3710062
age | .0249828 .0112566
> 2.22
> 0.026
> .0029202
> .0470453
hh_children | .1828249 .1377941
> 1.33
> 0.185
> -.0872466
> .4528964
consume_co~k | .0786529 .1530779
> 0.51
> 0.607
> -.2213743
> .3786801
consume_pl~k | .0480047 .1160054
> 0.41
> 0.679
> -.1793616
> .275371
consume_yo~t | -.2050149 .1420752
> -1.44
> 0.149
> -.4834773
> .0734474
milk_type_~d | .0181636 .1400629
> 0.13
> 0.897
> -.2563546
> .2926818
purchase_o~c | .0267056 .3738511
> 0.07
> 0.943
> -.7060291
> .7594403
primary_sh~r | -.1560629 .1774607
> -0.88
> 0.379
> -.5038796
> .1917537
lactose_in~t | .1148358 .3598271
> 0.32
> 0.750
> -.5904123
> .8200839
vegan | .1322243 .7298769
> 0.18
> 0.856
> -1.298308
> 1.562757
heard_ghg | .3934326 .6940635
> 0.57
> 0.571
> -.9669067
> 1.753772
heard_prob~s | .3665758 .8017325
> 0.46
> 0.648
> -1.204791
> 1.937943
probiotics~r | .1740225 .0970278
> 1.79
> 0.073
> -.0161484
> .3641935
ghg_mitiga~e | .0663172 .1128672
> 0.59
> 0.557
> -.1548984
> .2875328
imp_health~s | .0657331 .1861063
> 0.35
> 0.724
> -.2990286
> .4304948
imp_env_im~t | -.3263508 .1488582
> -2.19
> 0.028
> -.6181074
> -.0345942
imp_price | .0999826 .1602155
> 0.62
> 0.533
> -.2140339
> .4139992
gender | .122795 .2519376
> 0.49
> 0.626
> -.3709936
> .6165837
race | .3623889 .3453544
> 1.05
> 0.294
> -.3144933
> 1.039271
education | .1307857 .1362361
> 0.96
> 0.337
> -.1362322
> .3978036
hh_income | -.0613055 .0873386
> -0.70
> 0.483
> -.232486
> .109875
_cons | -23.35564 10.74737
> -2.17
> 0.030
> -44.4201
> -2.291176
----------------------------------------
> --------------------------------------
.
end of do-file
.
I just ran an analysis and the numbers in the table look very clumsy. They truncate and break into the next line randomly, making it difficult to easily match where a number starts, stops or continues.
I will appreciate some help on how to make numbers on tables in Stata output window more readable.
I have copied the result table below.
Thank you.
Iteration 0: log pseudolikelihood = -2
> 367.3412
Iteration 1: log pseudolikelihood = -2
> 325.3225
Iteration 2: log pseudolikelihood = -2
> 324.4821
Iteration 3: log pseudolikelihood = -
> 2324.481
Iteration 4: log pseudolikelihood = -
> 2324.481
Conditional logit choice model
> Number of obs
> = 7,668
Case ID variable: _caseid
> Number of cases
> = 2556
Alternatives variable: alt
> Alts per case: min = 3
> avg = 3.0
> max = 3
> Wald chi2(47)
> = 307.11
Log pseudolikelihood = -2324.481
> Prob > chi2
> = 0.0000
(Std. err. adjust
> ed for 213 clusters in participant_id)
----------------------------------------
> --------------------------------------
| Robust
choice_bin~y | Coefficient std. err.
> z
> P>|z|
> [95% con
> f. interval]
-------------+--------------------------
> --------------------------------------
alt |
probiotics | .5986185 .0926788
> 6.46
> 0.000
> .4169715
> .7802655
ghg | .1314119 .0247864
> 5.30
> 0.000
> .0828314
> .1799923
price | -.5302432 .0453643 -
> 11.69
> 0.000
> -.6191556
> -.4413309
-------------+--------------------------
> --------------------------------------
1 | (base alternative)
-------------+--------------------------
> --------------------------------------
2 |
treatment | .0794242 .1524742
> 0.52
> 0.602
> -.2194198
> .3782681
age | -.0040665 .0055171
> -0.74
> 0.461
> -.0148798
> .0067468
hh_children | -.1385581 .077862
> -1.78
> 0.075
> -.2911648
> .0140485
consume_co~k | .1144744 .0760616
> 1.51
> 0.132
> -.0346036
> .2635523
consume_pl~k | -.0060073 .059937
> -0.10
> 0.920
> -.1234816
> .111467
consume_yo~t | .029042 .0609137
> 0.48
> 0.634
> -.0903466
> .1484306
milk_type_~d | -.0913142 .0749532
> -1.22
> 0.223
> -.2382198
> .0555915
purchase_o~c | .673682 .1698245
> 3.97
> 0.000
> .3408321
> 1.006532
primary_sh~r | -.047291 .0974846
> -0.49
> 0.628
> -.2383572
> .1437753
lactose_in~t | .3394884 .1682233
> 2.02
> 0.044
> .0097767
> .6692001
vegan | .1019169 .5041907
> 0.20
> 0.840
> -.8862787
> 1.090112
heard_ghg | -.3259258 .303606
> -1.07
> 0.283
> -.9209825
> .2691309
heard_prob~s | 1.472352 .2881793
> 5.11
> 0.000
> .9075308
> 2.037173
probiotics~r | -.0816183 .0626449
> -1.30
> 0.193
> -.2044
> .0411635
ghg_mitiga~e | -.0281954 .0752327
> -0.37
> 0.708
> -.1756488
> .1192579
imp_health~s | .1371347 .1046537
> 1.31
> 0.190
> -.0679828
> .3422521
imp_env_im~t | .0627153 .0776906
> 0.81
> 0.420
> -.0895554
> .2149861
imp_price | -.1473768 .098069
> -1.50
> 0.133
> -.3395886
> .0448349
gender | -.1641028 .1399933
> -1.17
> 0.241
> -.4384847
> .1102791
race | .0232985 .1767956
> 0.13
> 0.895
> -.3232145
> .3698115
education | .0186446 .0613061
> 0.30
> 0.761
> -.1015131
> .1388024
hh_income | -.001493 .0422218
> -0.04
> 0.972
> -.0842462
> .0812602
_cons | -1.19653 1.024643
> -1.17
> 0.243
> -3.204793
> .8117321
-------------+--------------------------
> --------------------------------------
3 |
treatment | -.1707717 .2764224
> -0.62
> 0.537
> -.7125497
> .3710062
age | .0249828 .0112566
> 2.22
> 0.026
> .0029202
> .0470453
hh_children | .1828249 .1377941
> 1.33
> 0.185
> -.0872466
> .4528964
consume_co~k | .0786529 .1530779
> 0.51
> 0.607
> -.2213743
> .3786801
consume_pl~k | .0480047 .1160054
> 0.41
> 0.679
> -.1793616
> .275371
consume_yo~t | -.2050149 .1420752
> -1.44
> 0.149
> -.4834773
> .0734474
milk_type_~d | .0181636 .1400629
> 0.13
> 0.897
> -.2563546
> .2926818
purchase_o~c | .0267056 .3738511
> 0.07
> 0.943
> -.7060291
> .7594403
primary_sh~r | -.1560629 .1774607
> -0.88
> 0.379
> -.5038796
> .1917537
lactose_in~t | .1148358 .3598271
> 0.32
> 0.750
> -.5904123
> .8200839
vegan | .1322243 .7298769
> 0.18
> 0.856
> -1.298308
> 1.562757
heard_ghg | .3934326 .6940635
> 0.57
> 0.571
> -.9669067
> 1.753772
heard_prob~s | .3665758 .8017325
> 0.46
> 0.648
> -1.204791
> 1.937943
probiotics~r | .1740225 .0970278
> 1.79
> 0.073
> -.0161484
> .3641935
ghg_mitiga~e | .0663172 .1128672
> 0.59
> 0.557
> -.1548984
> .2875328
imp_health~s | .0657331 .1861063
> 0.35
> 0.724
> -.2990286
> .4304948
imp_env_im~t | -.3263508 .1488582
> -2.19
> 0.028
> -.6181074
> -.0345942
imp_price | .0999826 .1602155
> 0.62
> 0.533
> -.2140339
> .4139992
gender | .122795 .2519376
> 0.49
> 0.626
> -.3709936
> .6165837
race | .3623889 .3453544
> 1.05
> 0.294
> -.3144933
> 1.039271
education | .1307857 .1362361
> 0.96
> 0.337
> -.1362322
> .3978036
hh_income | -.0613055 .0873386
> -0.70
> 0.483
> -.232486
> .109875
_cons | -23.35564 10.74737
> -2.17
> 0.030
> -44.4201
> -2.291176
----------------------------------------
> --------------------------------------
.
end of do-file
.
Comment