---------------------------------------------------------------------------------------------------------------------------
name: <unnamed>
log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\fall_2021_logs\class12.log
log type: text
opened on: 27 Oct 2021, 09:39:07
. use "C:\Users\mexmi\Documents\current class files\intro soc methods\cps_mar_2000_new with additional vars.dta"
. *class starts here
. tabulate disabwrk disability, miss
| disability
Work disability | 0 1 . | Total
----------------------+---------------------------------+----------
NIU | 0 0 30,484 | 30,484
No disability that af | 93,260 0 0 | 93,260
Disability limits or | 0 9,966 0 | 9,966
----------------------+---------------------------------+----------
Total | 93,260 9,966 30,484 | 133,710
* Reminder: the disability variable I created just takes disabwrk and replaces the missing value code with “.” Which Stata understands as the missing value code.
* In this brief log I ventured into making some graphs of predicted values from the logistic model predicting disability. In logistic regression often the graph tells a better story than words might.
. logistic disability i.sex i.race ib2.metro yrsed c.age##c.age if age>25 & age<65, coef
Logistic regression Number of obs = 67,639
LR chi2(11) = 3165.03
Prob > chi2 = 0.0000
Log likelihood = -17987.159 Pseudo R2 = 0.0809
-----------------------------------------------------------------------------------------------
disability | Coefficient Std. err. z P>|z| [95% conf. interval]
------------------------------+----------------------------------------------------------------
sex |
Female | -.0053214 .0285513 -0.19 0.852 -.061281 .0506382
|
race |
Black/Negro | .6057231 .0422787 14.33 0.000 .5228584 .6885878
American Indian/Aleut/Eskimo | .3220015 .1122286 2.87 0.004 .1020374 .5419655
Asian or Pacific Islander | -.3954921 .0973523 -4.06 0.000 -.5862991 -.204685
|
metro |
Not identifiable | .2903771 .2356622 1.23 0.218 -.1715123 .7522665
Not in metro area | .0874893 .0411313 2.13 0.033 .0068734 .1681052
Outside central city | -.2155205 .038526 -5.59 0.000 -.2910301 -.1400109
Central city status unknown | .0362145 .0465509 0.78 0.437 -.0550236 .1274527
|
yrsed | -.1416333 .0041263 -34.32 0.000 -.1497206 -.1335459
age | .0661656 .0127446 5.19 0.000 .0411866 .0911445
|
c.age#c.age | -.000151 .0001364 -1.11 0.268 -.0004183 .0001163
|
_cons | -3.272449 .2905579 -11.26 0.000 -3.841932 -2.702966
-----------------------------------------------------------------------------------------------
* margins is a post-estimation command that will give us predicted values of disability at every combination of levels (here at every combination of age and race) with 95% CI. This produces a long table.
. margins race, over(age)
Predictive margins Number of obs = 67,639
Model VCE: OIM
Expression: Pr(disability), predict()
Over: age
--------------------------------------------------------------------------------------------------
| Delta-method
| Margin std. err. z P>|z| [95% conf. interval]
---------------------------------+----------------------------------------------------------------
age#race |
26#White | .0291362 .0015358 18.97 0.000 .026126 .0321464
26#Black/Negro | .0518156 .0031305 16.55 0.000 .0456799 .0579513
26#American Indian/Aleut/Eskimo | .0396503 .0046113 8.60 0.000 .0306122 .0486883
26#Asian or Pacific Islander | .01986 .002111 9.41 0.000 .0157225 .0239975
27#White | .0305848 .0014634 20.90 0.000 .0277166 .0334529
27#Black/Negro | .0542954 .0030586 17.75 0.000 .0483008 .0602901
27#American Indian/Aleut/Eskimo | .0415864 .004739 8.78 0.000 .032298 .0508747
27#Asian or Pacific Islander | .0208637 .0021652 9.64 0.000 .0166199 .0251076
28#White | .0319968 .0013882 23.05 0.000 .0292759 .0347176
28#Black/Negro | .0567449 .0029944 18.95 0.000 .0508761 .0626137
28#American Indian/Aleut/Eskimo | .0434861 .0048745 8.92 0.000 .0339322 .05304
28#Asian or Pacific Islander | .0218358 .0022203 9.83 0.000 .017484 .0261876
29#White | .0337242 .001323 25.49 0.000 .0311311 .0363173
29#Black/Negro | .0596711 .0029502 20.23 0.000 .0538888 .0654535
29#American Indian/Aleut/Eskimo | .0457841 .0050438 9.08 0.000 .0358984 .0556698
29#Asian or Pacific Islander | .0230374 .0022986 10.02 0.000 .0185323 .0275426
30#White | .0363883 .0012882 28.25 0.000 .0338635 .0389131
30#Black/Negro | .064178 .002984 21.51 0.000 .0583295 .0700264
30#American Indian/Aleut/Eskimo | .0493253 .0053436 9.23 0.000 .038852 .0597986
30#Asian or Pacific Islander | .0248925 .0024406 10.20 0.000 .0201091 .0296759
31#White | .0369098 .0011845 31.16 0.000 .0345882 .0392314
31#Black/Negro | .0651022 .0028879 22.54 0.000 .0594421 .0707623
31#American Indian/Aleut/Eskimo | .0500336 .0053733 9.31 0.000 .0395021 .060565
31#Asian or Pacific Islander | .025249 .0024474 10.32 0.000 .0204521 .0300458
32#White | .0395739 .0011513 34.37 0.000 .0373174 .0418304
32#Black/Negro | .0696257 .0029481 23.62 0.000 .0638476 .0754037
32#American Indian/Aleut/Eskimo | .0535815 .0056849 9.43 0.000 .0424393 .0647237
32#Asian or Pacific Islander | .0271003 .0025966 10.44 0.000 .022011 .0321897
33#White | .0413991 .0011022 37.56 0.000 .0392389 .0435594
33#Black/Negro | .0727883 .002967 24.53 0.000 .066973 .0786035
33#American Indian/Aleut/Eskimo | .056037 .0059059 9.49 0.000 .0444617 .0676124
33#Asian or Pacific Islander | .0283558 .0026954 10.52 0.000 .0230729 .0336388
34#White | .0445962 .0010932 40.79 0.000 .0424536 .0467389
34#Black/Negro | .0780255 .0030397 25.67 0.000 .0720678 .0839832
34#American Indian/Aleut/Eskimo | .0602217 .0062642 9.61 0.000 .0479441 .0724993
34#Asian or Pacific Islander | .0306145 .0028744 10.65 0.000 .0249808 .0362482
35#White | .0473475 .0010779 43.93 0.000 .0452348 .0494601
35#Black/Negro | .0825975 .0031402 26.30 0.000 .0764429 .0887521
35#American Indian/Aleut/Eskimo | .0638474 .0065812 9.70 0.000 .0509485 .0767463
35#Asian or Pacific Islander | .032546 .0030367 10.72 0.000 .0265941 .0384978
36#White | .0501405 .0010791 46.47 0.000 .0480255 .0522555
36#Black/Negro | .0873828 .0032684 26.74 0.000 .080977 .0937887
36#American Indian/Aleut/Eskimo | .0675849 .0069397 9.74 0.000 .0539833 .0811865
36#Asian or Pacific Islander | .0344767 .0032065 10.75 0.000 .028192 .0407613
37#White | .051982 .0010748 48.36 0.000 .0498754 .0540886
37#Black/Negro | .09035 .0033095 27.30 0.000 .0838636 .0968364
37#American Indian/Aleut/Eskimo | .0699757 .0071446 9.79 0.000 .0559726 .0839789
37#Asian or Pacific Islander | .035788 .0033088 10.82 0.000 .0293028 .0422732
38#White | .0533723 .0010736 49.71 0.000 .0512681 .0554765
38#Black/Negro | .0928188 .0033957 27.33 0.000 .0861633 .0994742
38#American Indian/Aleut/Eskimo | .0718698 .0073364 9.80 0.000 .0574907 .086249
38#Asian or Pacific Islander | .0367317 .0033991 10.81 0.000 .0300696 .0433939
39#White | .056669 .0011196 50.62 0.000 .0544746 .0588633
39#Black/Negro | .0982141 .0035574 27.61 0.000 .0912417 .1051864
39#American Indian/Aleut/Eskimo | .0761828 .0077235 9.86 0.000 .0610449 .0913206
39#Asian or Pacific Islander | .0390621 .0036002 10.85 0.000 .0320058 .0461184
40#White | .0627902 .0012322 50.96 0.000 .0603752 .0652053
40#Black/Negro | .1079537 .0038236 28.23 0.000 .1004597 .1154478
40#American Indian/Aleut/Eskimo | .0840833 .0084038 10.01 0.000 .0676122 .1005545
40#Asian or Pacific Islander | .0434445 .0039665 10.95 0.000 .0356703 .0512186
41#White | .0638476 .0012504 51.06 0.000 .0613969 .0662983
41#Black/Negro | .1099607 .0039287 27.99 0.000 .1022607 .1176608
41#American Indian/Aleut/Eskimo | .0855765 .0085802 9.97 0.000 .0687597 .1023934
41#Asian or Pacific Islander | .0441327 .0040439 10.91 0.000 .0362068 .0520586
42#White | .067079 .0013121 51.12 0.000 .0645074 .0696507
42#Black/Negro | .1150673 .0040858 28.16 0.000 .1070593 .1230752
42#American Indian/Aleut/Eskimo | .0897333 .0089318 10.05 0.000 .0722273 .1072393
42#Asian or Pacific Islander | .0464539 .0042398 10.96 0.000 .0381442 .0547637
43#White | .0703614 .0013809 50.95 0.000 .0676548 .073068
43#Black/Negro | .1202497 .004228 28.44 0.000 .111963 .1285364
43#American Indian/Aleut/Eskimo | .0939533 .0092895 10.11 0.000 .0757462 .1121603
43#Asian or Pacific Islander | .0488134 .0044364 11.00 0.000 .0401181 .0575086
44#White | .0735196 .0014369 51.17 0.000 .0707034 .0763358
44#Black/Negro | .1251446 .0043825 28.56 0.000 .116555 .1337342
44#American Indian/Aleut/Eskimo | .0979728 .0096265 10.18 0.000 .0791051 .1168404
44#Asian or Pacific Islander | .0511109 .0046218 11.06 0.000 .0420523 .0601695
45#White | .0768419 .001496 51.36 0.000 .0739097 .079774
45#Black/Negro | .1304322 .0045495 28.67 0.000 .1215153 .139349
45#American Indian/Aleut/Eskimo | .1022643 .0099922 10.23 0.000 .0826801 .1218486
45#Asian or Pacific Islander | .0534845 .0048258 11.08 0.000 .0440261 .0629429
46#White | .0801878 .0015538 51.61 0.000 .0771425 .0832331
46#Black/Negro | .1359156 .0047322 28.72 0.000 .1266407 .1451905
46#American Indian/Aleut/Eskimo | .1066487 .0103896 10.26 0.000 .0862854 .127012
46#Asian or Pacific Islander | .0558423 .005036 11.09 0.000 .0459718 .0657127
47#White | .0837023 .001602 52.25 0.000 .0805625 .0868421
47#Black/Negro | .1412271 .0048535 29.10 0.000 .1317145 .1507397
47#American Indian/Aleut/Eskimo | .1110696 .0107282 10.35 0.000 .0900427 .1320966
47#Asian or Pacific Islander | .0584255 .0052339 11.16 0.000 .0481672 .0686838
48#White | .0875746 .0016564 52.87 0.000 .0843281 .0908211
48#Black/Negro | .1475587 .0050432 29.26 0.000 .1376742 .1574432
48#American Indian/Aleut/Eskimo | .1161428 .0111854 10.38 0.000 .0942198 .1380658
48#Asian or Pacific Islander | .0611489 .0054759 11.17 0.000 .0504164 .0718814
49#White | .0912184 .001688 54.04 0.000 .08791 .0945268
49#Black/Negro | .1527887 .0051215 29.83 0.000 .1427507 .1628266
49#American Indian/Aleut/Eskimo | .1206095 .0114919 10.50 0.000 .0980857 .1431332
49#Asian or Pacific Islander | .0638987 .0056647 11.28 0.000 .0527961 .0750014
50#White | .0961123 .0017368 55.34 0.000 .0927082 .0995164
50#Black/Negro | .1605574 .0053374 30.08 0.000 .1500964 .1710185
50#American Indian/Aleut/Eskimo | .1269285 .0120227 10.56 0.000 .1033644 .1504926
50#Asian or Pacific Islander | .0673901 .0059618 11.30 0.000 .0557052 .079075
51#White | .1006579 .0017704 56.85 0.000 .0971879 .1041279
51#Black/Negro | .1674929 .0055066 30.42 0.000 .1567003 .1782856
51#American Indian/Aleut/Eskimo | .1326787 .0124718 10.64 0.000 .1082345 .157123
51#Asian or Pacific Islander | .0707063 .0062234 11.36 0.000 .0585086 .0829039
52#White | .1055833 .0018104 58.32 0.000 .1020349 .1091316
52#Black/Negro | .1751472 .0057009 30.72 0.000 .1639735 .1863208
52#American Indian/Aleut/Eskimo | .1389699 .0129668 10.72 0.000 .1135555 .1643843
52#Asian or Pacific Islander | .074261 .0065177 11.39 0.000 .0614866 .0870354
53#White | .1103685 .0018447 59.83 0.000 .106753 .113984
53#Black/Negro | .1821939 .0058667 31.06 0.000 .1706954 .1936923
53#American Indian/Aleut/Eskimo | .144911 .013386 10.83 0.000 .1186749 .171147
53#Asian or Pacific Islander | .0778288 .0067843 11.47 0.000 .0645318 .0911258
54#White | .1202041 .0019636 61.22 0.000 .1163555 .1240527
54#Black/Negro | .1966507 .0061444 32.00 0.000 .1846079 .2086935
54#American Indian/Aleut/Eskimo | .1571211 .014253 11.02 0.000 .1291858 .1850565
54#Asian or Pacific Islander | .0851462 .0073274 11.62 0.000 .0707848 .0995077
55#White | .1276141 .0020584 62.00 0.000 .1235797 .1316484
55#Black/Negro | .2071298 .0063832 32.45 0.000 .1946189 .2196406
55#American Indian/Aleut/Eskimo | .1661403 .0148239 11.21 0.000 .137086 .1951946
55#Asian or Pacific Islander | .0907794 .0077337 11.74 0.000 .0756215 .1059372
56#White | .1328917 .0021776 61.03 0.000 .1286238 .1371596
56#Black/Negro | .2153072 .0066274 32.49 0.000 .2023177 .2282967
56#American Indian/Aleut/Eskimo | .1728806 .0153955 11.23 0.000 .142706 .2030552
56#Asian or Pacific Islander | .0945764 .0080519 11.75 0.000 .078795 .1103578
57#White | .1395999 .0023434 59.57 0.000 .1350071 .1441928
57#Black/Negro | .2246018 .0069049 32.53 0.000 .2110684 .2381352
57#American Indian/Aleut/Eskimo | .1809549 .0159014 11.38 0.000 .1497889 .212121
57#Asian or Pacific Islander | .0997462 .0084266 11.84 0.000 .0832303 .116262
58#White | .1503938 .0026495 56.76 0.000 .1452008 .1555867
58#Black/Negro | .2402677 .0073436 32.72 0.000 .2258746 .2546609
58#American Indian/Aleut/Eskimo | .1942938 .0168469 11.53 0.000 .1612744 .2273132
58#Asian or Pacific Islander | .107783 .0090619 11.89 0.000 .0900221 .1255439
59#White | .1548894 .0029281 52.90 0.000 .1491505 .1606284
59#Black/Negro | .2467438 .0076597 32.21 0.000 .231731 .2617565
59#American Indian/Aleut/Eskimo | .1998296 .017286 11.56 0.000 .1659497 .2337096
59#Asian or Pacific Islander | .11114 .0093554 11.88 0.000 .0928038 .1294761
60#White | .1621137 .0033307 48.67 0.000 .1555857 .1686418
60#Black/Negro | .2570016 .0080902 31.77 0.000 .2411452 .272858
60#American Indian/Aleut/Eskimo | .2086489 .0179467 11.63 0.000 .1734741 .2438237
60#Asian or Pacific Islander | .116605 .0098046 11.89 0.000 .0973883 .1358216
61#White | .1714402 .0038294 44.77 0.000 .1639348 .1789457
61#Black/Negro | .2692025 .0085747 31.40 0.000 .2523964 .2860086
61#American Indian/Aleut/Eskimo | .2195731 .0186038 11.80 0.000 .1831103 .256036
61#Asian or Pacific Islander | .1239699 .010352 11.98 0.000 .1036803 .1442595
62#White | .1818269 .0044742 40.64 0.000 .1730576 .1905962
62#Black/Negro | .2839796 .0093362 30.42 0.000 .265681 .3022782
62#American Indian/Aleut/Eskimo | .2322845 .0195918 11.86 0.000 .1938853 .2706836
62#Asian or Pacific Islander | .1317758 .0110513 11.92 0.000 .1101157 .153436
63#White | .1930082 .0051641 37.38 0.000 .1828868 .2031296
63#Black/Negro | .297791 .0099591 29.90 0.000 .2782715 .3173106
63#American Indian/Aleut/Eskimo | .2449851 .0202723 12.08 0.000 .2052522 .2847181
63#Asian or Pacific Islander | .14093 .0117012 12.04 0.000 .1179961 .1638639
64#White | .1981591 .0058617 33.81 0.000 .1866703 .2096478
64#Black/Negro | .3045623 .0106882 28.50 0.000 .2836137 .3255108
64#American Indian/Aleut/Eskimo | .2510348 .0208096 12.06 0.000 .2102487 .291821
64#Asian or Pacific Islander | .1449857 .0121485 11.93 0.000 .121175 .1687963
--------------------------------------------------------------------------------------------------
* the marginsplot command turns the long table into a useful graph (which is not embedded in this text file but you can find it in the
. marginsplot, x(age)
Variables that uniquely identify margins: race age
* Now let’s try to make a graph just of the White subjects of actual versus predicted disability by age:
. by age: egen wht_disability_mean=mean(disability) if race==100
(45,376 missing values generated)
* The by age: egen command lets us attribute an average (within each year of age) of disability to each white person, so we can graph the averages rather than the flurry of 0’s and 1’s which would not be helpful.
. logistic disability i.sex i.race ib2.metro yrsed c.age##c.age if age>25 & age<65, coef
Logistic regression Number of obs = 67,639
LR chi2(11) = 3165.03
Prob > chi2 = 0.0000
Log likelihood = -17987.159 Pseudo R2 = 0.0809
-----------------------------------------------------------------------------------------------
disability | Coefficient Std. err. z P>|z| [95% conf. interval]
------------------------------+----------------------------------------------------------------
sex |
Female | -.0053214 .0285513 -0.19 0.852 -.061281 .0506382
|
race |
Black/Negro | .6057231 .0422787 14.33 0.000 .5228584 .6885878
American Indian/Aleut/Eskimo | .3220015 .1122286 2.87 0.004 .1020374 .5419655
Asian or Pacific Islander | -.3954921 .0973523 -4.06 0.000 -.5862991 -.204685
|
metro |
Not identifiable | .2903771 .2356622 1.23 0.218 -.1715123 .7522665
Not in metro area | .0874893 .0411313 2.13 0.033 .0068734 .1681052
Outside central city | -.2155205 .038526 -5.59 0.000 -.2910301 -.1400109
Central city status unknown | .0362145 .0465509 0.78 0.437 -.0550236 .1274527
|
yrsed | -.1416333 .0041263 -34.32 0.000 -.1497206 -.1335459
age | .0661656 .0127446 5.19 0.000 .0411866 .0911445
|
c.age#c.age | -.000151 .0001364 -1.11 0.268 -.0004183 .0001163
|
_cons | -3.272449 .2905579 -11.26 0.000 -3.841932 -2.702966
-----------------------------------------------------------------------------------------------
* Now I am going to use the model to generate predicted disability averages for White people.
. predict wht_disab_predicted if age>25 & age<65 & race==100
(option pr assumed; Pr(disability))
(75,824 missing values generated)
* And here below I am using by age: egen to generate the average (at each year of age) of predicted values of disability for White people
. by age: egen wht_disability_predicted=mean( wht_disab_predicted ) if race==100
(75,824 missing values generated)
* And here below I graph the actual and predicted values together.
. twoway (connected wht_disability_predicted age if age>25 & age<65) (scatter wht_disability_mean age if age>25 & age<65, mcolor(dknavy) msymbol(smx))
* After fidgeting with the colors a little in the Stata graph editor, it looks like this:
. log close
name: <unnamed>
log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\fall_2021_logs\class12.log
log type: text
closed on: 27 Oct 2021, 14:53:56
---------------------------------------------------------------------------------------------------------------------------