Code:
. glm A c1r c1l c1c B C D E F G H I J K L M N O i.country, fa(b) link(logit) vce(robust)
note: vote1 has noninteger values
Iteration 0: log pseudolikelihood = -42.94693
Iteration 1: log pseudolikelihood = -42.86314
Iteration 2: log pseudolikelihood = -42.862902
Iteration 3: log pseudolikelihood = -42.862902
Generalized linear models No. of obs = 109
Optimization : ML Residual df = 66
Scale parameter = 1
Deviance = 2.554759242 (1/df) Deviance = .0387085
Pearson = 2.449876389 (1/df) Pearson = .0371193
Variance function: V(u) = u*(1-u/1) [Binomial]
Link function : g(u) = ln(u/(1-u)) [Logit]
AIC = 1.575466
Log pseudolikelihood = -42.86290183 BIC = -307.0742
-------------------------------------------------------------------------------------
| Robust
A | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
c1r | -8.777873 6.446331 -1.36 0.173 -21.41245 3.856704
c1l | -5.230066 9.232645 -0.57 0.571 -23.32572 12.86559
c1c | 14.04833 12.72731 1.10 0.270 -10.89674 38.9934
B | .5111221 1.002131 0.51 0.610 -1.453019 2.475264
C | -.2158331 .1219806 -1.77 0.077 -.4549108 .0232445
D | -1.015791 .6120185 -1.66 0.097 -2.215326 .1837427
E | -1.34568 .6942753 -1.94 0.053 -2.706434 .0150751
F | 1.913601 1.263446 1.51 0.130 -.5627074 4.389909
G | 3.03058 1.415898 2.14 0.032 .2554705 5.80569
H | -1.232532 .465843 -2.65 0.008 -2.145567 -.3194963
I | 9.664852 5.081045 1.90 0.057 -.2938134 19.62352
J | -6.138072 5.524545 -1.11 0.267 -16.96598 4.689838
K | 6.307305 2.485234 2.54 0.011 1.436336 11.17827
L | .2637226 .1204656 2.19 0.029 .0276144 .4998309
M | .0157357 .0524758 0.30 0.764 -.087115 .1185864
N | -5.59704 1.892737 -2.96 0.003 -9.306737 -1.887342
O | 1.622794 1.562146 1.04 0.299 -1.438957 4.684544
|
country |
2 | -.3174748 .2825339 -1.12 0.261 -.8712311 .2362816
3 | .9050205 .5325704 1.70 0.089 -.1387983 1.948839
5 | .6366366 .3458818 1.84 0.066 -.0412793 1.314553
6 | -.8061525 .3245252 -2.48 0.013 -1.44221 -.1700948
7 | .9979821 .5256645 1.90 0.058 -.0323014 2.028266
8 | -.3019033 .2208869 -1.37 0.172 -.7348336 .1310271
9 | 1.793135 1.120034 1.60 0.109 -.4020915 3.988362
10 | 2.935015 1.670142 1.76 0.079 -.3384039 6.208433
11 | 1.032755 .2682285 3.85 0.000 .5070367 1.558473
12 | .6142258 .4606796 1.33 0.182 -.2886895 1.517141
13 | .6579136 .2909312 2.26 0.024 .0876989 1.228128
14 | 2.322749 1.06672 2.18 0.029 .232017 4.413481
15 | .3860536 .4793302 0.81 0.421 -.5534164 1.325524
16 | .3461292 .6025894 0.57 0.566 -.8349244 1.527183
17 | -.4832077 .7065197 -0.68 0.494 -1.867961 .9015454
18 | 1.483425 .3839561 3.86 0.000 .7308851 2.235965
19 | .0972213 .3887099 0.25 0.803 -.664636 .8590787
20 | .8722559 .5144817 1.70 0.090 -.1361096 1.880621
21 | .846566 .3115486 2.72 0.007 .2359419 1.45719
22 | 1.283534 .5730708 2.24 0.025 .1603358 2.406732
23 | 1.252627 .498347 2.51 0.012 .2758851 2.229369
24 | .4272264 .3648452 1.17 0.242 -.2878572 1.14231
25 | 2.1732 .8145399 2.67 0.008 .5767307 3.769668
26 | -.5704112 .3033152 -1.88 0.060 -1.164898 .0240757
27 | 1.839924 1.113983 1.65 0.099 -.3434435 4.023291
|
_cons | -3.763331 1.28113 -2.94 0.003 -6.2743 -1.252362
-------------------------------------------------------------------------------------
.
Code:
glm A c.c1#D c.c1#E c.c1#creelection1 B i.C i.D i.E c.averagegovtexp#D c.averagegovtexp#E c.a
> veragegovtexp#creelection1 c.I c.J c.K c.L c.M c.N c.O i.country, fa(b) link(logit) vce(robus
> t)
note: 1.E#c.c1 omitted because of collinearity
note: 1.creelection1#c.c1 omitted because of collinearity
note: 1.E#c.averagegovtexp omitted because of collinearity
note: 1.creelection1#c.averagegovtexp omitted because of collinearity
note: A has noninteger values
Iteration 0: log pseudolikelihood = -42.941667
Iteration 1: log pseudolikelihood = -42.859938
Iteration 2: log pseudolikelihood = -42.8597
Iteration 3: log pseudolikelihood = -42.8597
Generalized linear models No. of obs = 109
Optimization : ML Residual df = 64
Scale parameter = 1
Deviance = 2.548356364 (1/df) Deviance = .0398181
Pearson = 2.444726741 (1/df) Pearson = .0381989
Variance function: V(u) = u*(1-u/1) [Binomial]
Link function : g(u) = ln(u/(1-u)) [Logit]
AIC = 1.612105
Log pseudolikelihood = -42.85970039 BIC = -297.6979
-------------------------------------------------------------------------------------
| Robust
A | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
rreelection1#c.c1 |
0 | 8.470525 19.76285 0.43 0.668 -30.26394 47.205
1 | -1.580529 23.87824 -0.07 0.947 -48.38102 45.21996
|
lreelection1#c.c1 |
0 | 6.771615 12.02922 0.56 0.573 -16.80523 30.34846
1 | 0 (omitted)
|
creelection1#c.c1 |
0 | -13.67147 15.70461 -0.87 0.384 -44.45194 17.10901
1 | 0 (omitted)
|
B | .5041213 1.014915 0.50 0.619 -1.485075 2.493317
1.C | -.2165275 .1214293 -1.78 0.075 -.4545245 .0214696
1.D | -1.076306 .632389 -1.70 0.089 -2.315766 .1631532
1.E | -1.412453 .6621664 -2.13 0.033 -2.710276 -.1146312
|
D#|
c.averagegovtexp |
0 | 1.571097 2.247716 0.70 0.485 -2.834346 5.97654
1 | 3.604304 2.404048 1.50 0.134 -1.107542 8.316151
|
E#|
c.averagegovtexp |
0 | -3.163227 1.347199 -2.35 0.019 -5.803689 -.5227646
1 | 0 (omitted)
|
creelection1#|
c.averagegovtexp |
0 | 1.20869 .519934 2.32 0.020 .1896384 2.227742
1 | 0 (omitted)
|
I | 9.703945 4.878562 1.99 0.047 .1421387 19.26575
J | -6.184813 5.818585 -1.06 0.288 -17.58903 5.219403
K | 6.0959 2.741294 2.22 0.026 .7230625 11.46874
L | .2592149 .1332625 1.95 0.052 -.0019748 .5204045
M | .024784 .063311 0.39 0.695 -.0993032 .1488713
N | -5.528738 1.958356 -2.82 0.005 -9.367046 -1.69043
O | 1.596015 1.553947 1.03 0.304 -1.449666 4.641695
|
country |
2 | -.3206109 .2972785 -1.08 0.281 -.903266 .2620443
3 | .7806132 .6643167 1.18 0.240 -.5214236 2.08265
5 | .5959516 .4866569 1.22 0.221 -.3578784 1.549781
6 | -.7933917 .3294559 -2.41 0.016 -1.439113 -.1476701
7 | .9247078 .6654651 1.39 0.165 -.3795798 2.228995
8 | -.3031683 .2269195 -1.34 0.182 -.7479223 .1415857
9 | 1.808557 1.184715 1.53 0.127 -.5134411 4.130555
10 | 2.941216 1.806134 1.63 0.103 -.5987413 6.481174
11 | 1.011056 .3283157 3.08 0.002 .3675687 1.654543
12 | .5916792 .5207664 1.14 0.256 -.4290042 1.612363
13 | .611112 .3838553 1.59 0.111 -.1412306 1.363454
14 | 2.317612 1.153793 2.01 0.045 .0562189 4.579004
15 | .2948902 .6324098 0.47 0.641 -.9446103 1.534391
16 | .2832857 .7178377 0.39 0.693 -1.12365 1.690222
17 | -.4926725 .7168042 -0.69 0.492 -1.897583 .9122379
18 | 1.447373 .4602922 3.14 0.002 .5452173 2.349529
19 | .0948476 .443375 0.21 0.831 -.7741515 .9638466
20 | .8335054 .5799551 1.44 0.151 -.3031856 1.970196
21 | .8195496 .3933186 2.08 0.037 .0486594 1.59044
22 | 1.217881 .7061443 1.72 0.085 -.1661364 2.601898
23 | 1.207571 .6129117 1.97 0.049 .0062862 2.408856
24 | .4207905 .4571189 0.92 0.357 -.475146 1.316727
25 | 2.149276 .9624491 2.23 0.026 .2629099 4.035641
26 | -.5625191 .294506 -1.91 0.056 -1.13974 .014702
27 | 1.804801 1.245022 1.45 0.147 -.6353973 4.244999
|
_cons | -3.550846 1.789157 -1.98 0.047 -7.05753 -.0441618
-------------------------------------------------------------------------------------
.
end of do-file
0 Response to Marginal Effects with Factor Variables
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