Coefficient plot for 2 groups in same plot

[amera amery]

Hello All,

I have results for the regression where the variable is intersection between two variables days#Buisness_policy. The results as follows. I want to plot results of coefficient using two line first when Buisness_policy=0 and the other for buisness_policy=1. Any help please?

MWFE estimator converged in 24 iterations)
note: 368.days#0b.busniss_policy omitted because of collinearity
note: 368.days#1.busniss_policy omitted because of collinearity

HDFE Linear regression Number of obs = 3,590
Absorbing 2 HDFE groups F( 79, 587) = 2.18
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.6279
Adj R-squared = 0.5140
Within R-sq. = 0.0370
Number of clusters (fips) = 588 Root MSE = 0.4253

(Std. err. adjusted for 588 clusters in fips)
-------------------------------------------------------------------------------
| Robust
cases_normed | Coefficient std. err. t P>|t| [95% conf. interval]
--------------+----------------------------------------------------------------
days#|
busniss_pol~y |
331 1 | -.4099208 .1463507 -2.80 0.005 -.6973556 -.1224861
332 0 | -.0280733 .0837722 -0.34 0.738 -.192603 .1364564
332 1 | -.4984619 .1224785 -4.07 0.000 -.7390113 -.2579125
333 0 | -.0616372 .0700387 -0.88 0.379 -.1991941 .0759198
333 1 | -.5363874 .185835 -2.89 0.004 -.9013698 -.1714049
334 0 | -.0225765 .0739121 -0.31 0.760 -.1677409 .1225878
334 1 | -.3172599 .1398723 -2.27 0.024 -.5919711 -.0425487
335 0 | -.0933705 .0597221 -1.56 0.118 -.2106655 .0239244
335 1 | -.5004138 .1193204 -4.19 0.000 -.7347608 -.2660669
336 0 | .0967768 .0701418 1.38 0.168 -.0409828 .2345363
336 1 | -.4009202 .1458857 -2.75 0.006 -.6874417 -.1143988
337 0 | -.0125092 .0618269 -0.20 0.840 -.1339381 .1089197
337 1 | -.4015682 .1228618 -3.27 0.001 -.6428705 -.160266
338 0 | .0315365 .0510951 0.62 0.537 -.0688151 .1318881
338 1 | -.2958461 .1156456 -2.56 0.011 -.5229756 -.0687165
339 0 | .042306 .0674217 0.63 0.531 -.0901112 .1747232
339 1 | -.3720456 .1555316 -2.39 0.017 -.6775117 -.0665794
340 0 | -.0115566 .0715844 -0.16 0.872 -.1521494 .1290362
340 1 | -.3557411 .1119916 -3.18 0.002 -.5756941 -.135788
341 0 | -.0185854 .0583593 -0.32 0.750 -.1332039 .096033
341 1 | .0148648 .1622708 0.09 0.927 -.3038373 .333567
342 0 | .1023682 .0698598 1.47 0.143 -.0348373 .2395738
342 1 | -.2631451 .1163144 -2.26 0.024 -.4915882 -.0347021
343 0 | .0740682 .0575279 1.29 0.198 -.0389175 .1870538
343 1 | -.045251 .2053903 -0.22 0.826 -.4486403 .3581383
344 0 | .1176535 .1101376 1.07 0.286 -.0986582 .3339652
344 1 | -.2291802 .1020424 -2.25 0.025 -.4295929 -.0287674
345 0 | .0408045 .0563465 0.72 0.469 -.0698607 .1514698
345 1 | -.2384194 .1255983 -1.90 0.058 -.4850961 .0082573
346 0 | .0222127 .0756599 0.29 0.769 -.1263844 .1708097
346 1 | -.2546369 .1326608 -1.92 0.055 -.5151845 .0059107
347 0 | .1153542 .0639572 1.80 0.072 -.0102585 .240967
347 1 | -.3069842 .1106948 -2.77 0.006 -.5243902 -.0895781
348 0 | .0553402 .0593864 0.93 0.352 -.0612955 .1719759
348 1 | -.0976982 .1296248 -0.75 0.451 -.352283 .1568866
349 0 | .1930237 .0584035 3.30 0.001 .0783184 .307729
349 1 | -.2138118 .1374718 -1.56 0.120 -.4838084 .0561847
350 0 | .1445166 .0630535 2.29 0.022 .0206787 .2683546
350 1 | -.2743005 .128843 -2.13 0.034 -.52735 -.021251
351 0 | -.0289425 .0635516 -0.46 0.649 -.1537586 .0958737
351 1 | -.1453901 .1220631 -1.19 0.234 -.3851236 .0943435
352 0 | .214769 .0776344 2.77 0.006 .062294 .367244
352 1 | -.3472789 .1164588 -2.98 0.003 -.5760056 -.1185521
353 0 | .0450266 .0625553 0.72 0.472 -.0778328 .167886
353 1 | -.1798121 .1141795 -1.57 0.116 -.4040622 .0444379
354 0 | .2014349 .0754679 2.67 0.008 .053215 .3496548
354 1 | -.1796731 .1079783 -1.66 0.097 -.391744 .0323979
355 0 | .1087534 .054784 1.99 0.048 .0011569 .2163499
355 1 | -.2302023 .1196702 -1.92 0.055 -.4652361 .0048315
356 0 | .2824935 .107707 2.62 0.009 .0709554 .4940316
356 1 | -.2231089 .1538338 -1.45 0.148 -.5252405 .0790227
357 0 | .1547178 .0556484 2.78 0.006 .0454236 .2640121
357 1 | -.0782992 .1081413 -0.72 0.469 -.2906902 .1340919
358 0 | .1349423 .0531275 2.54 0.011 .0305992 .2392854
358 1 | -.1575049 .1127381 -1.40 0.163 -.3789242 .0639143
359 0 | .1751728 .0602025 2.91 0.004 .0569343 .2934113
359 1 | -.1489528 .0987957 -1.51 0.132 -.3429889 .0450833
360 0 | .1098763 .0616988 1.78 0.075 -.011301 .2310537
360 1 | -.1354763 .1137266 -1.19 0.234 -.3588368 .0878843
361 0 | .2387945 .0745974 3.20 0.001 .0922842 .3853047
361 1 | -.2109066 .1231149 -1.71 0.087 -.452706 .0308928
362 0 | .002561 .076416 0.03 0.973 -.1475211 .152643
362 1 | -.3449196 .1387402 -2.49 0.013 -.6174073 -.0724319
363 0 | .1106431 .0626451 1.77 0.078 -.0123926 .2336789
363 1 | -.1642916 .099942 -1.64 0.101 -.360579 .0319959
364 0 | .1120981 .0751653 1.49 0.136 -.0355276 .2597238
364 1 | -.1138568 .1051597 -1.08 0.279 -.3203919 .0926783
365 0 | .0224863 .0770132 0.29 0.770 -.1287687 .1737414
365 1 | .0063751 .1392956 0.05 0.964 -.2672033 .2799534
366 0 | .0786756 .0677887 1.16 0.246 -.0544624 .2118135
366 1 | -.1137175 .1497857 -0.76 0.448 -.4078987 .1804638
367 0 | .0472342 .0662716 0.71 0.476 -.0829241 .1773926
367 1 | -.0897144 .0820489 -1.09 0.275 -.2508596 .0714307
368 0 | 0 (omitted)
368 1 | 0 (omitted)
|
residential | -.6372152 .5779848 -1.10 0.271 -1.772385 .4979547
workplaces | -.3516455 .2013581 -1.75 0.081 -.7471155 .0438246
transit_sta~s | .0581689 .0414588 1.40 0.161 -.0232568 .1395946
parks | -.0022952 .0263351 -0.09 0.931 -.0540177 .0494272
grocery_and~y | .2286835 .1573574 1.45 0.147 -.0803686 .5377356
retail_and_~n | -.0912527 .1100078 -0.83 0.407 -.3073095 .1248042
_cons | .5892569 .7971742 0.74 0.460 -.976404 2.154918
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
fips | 588 588 0 *|
time | 175 0 175 |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation