The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
margins is intended as a port of (some of) the
features of Stata’s margins command. This vignette compares
output from Stata’s margins command for linear models
against the output of margins.
library("margins")
options(width = 100). quietly reg mpg cyl hp wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OLS
Expression : Linear prediction, predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | -.9416166 .5509165 -1.71 0.098 -2.070118 .1868846
hp | -.0180381 .0118763 -1.52 0.140 -.0423655 .0062893
wt | -3.166973 .740576 -4.28 0.000 -4.683975 -1.649972
------------------------------------------------------------------------------library("margins")
x <- lm(mpg ~ cyl + hp + wt, data = mtcars)
summary(margins(x))## factor AME SE z p lower upper
## cyl -0.9416 0.5509 -1.7092 0.0874 -2.0214 0.1382
## hp -0.0180 0.0119 -1.5188 0.1288 -0.0413 0.0052
## wt -3.1670 0.7406 -4.2764 0.0000 -4.6185 -1.7155. quietly reg mpg cyl c.hp##c.wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OLS
Expression : Linear prediction, predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | -.3652391 .5086204 -0.72 0.479 -1.408842 .6783638
hp | -.0252715 .0105097 -2.40 0.023 -.0468357 -.0037073
wt | -3.837584 .6730996 -5.70 0.000 -5.21867 -2.456498
------------------------------------------------------------------------------x <- lm(mpg ~ cyl + hp * wt, data = mtcars)
summary(margins(x))## factor AME SE z p lower upper
## cyl -0.3652 0.5086 -0.7181 0.4727 -1.3621 0.6316
## hp -0.0253 0.0105 -2.4046 0.0162 -0.0459 -0.0047
## wt -3.8376 0.6731 -5.7014 0.0000 -5.1568 -2.5183. quietly reg mpg i.cyl hp wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OLS
Expression : Linear prediction, predict()
dy/dx w.r.t. : 6.cyl 8.cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl |
6 | -3.359024 1.40167 -2.40 0.024 -6.235014 -.4830353
8 | -3.185884 2.170476 -1.47 0.154 -7.639332 1.267564
|
hp | -.0231198 .0119522 -1.93 0.064 -.0476437 .0014041
wt | -3.181404 .7196011 -4.42 0.000 -4.657904 -1.704905
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.x <- lm(mpg ~ factor(cyl) + hp + wt, data = mtcars)
summary(margins(x))## factor AME SE z p lower upper
## cyl6 -3.3590 1.4017 -2.3964 0.0166 -6.1062 -0.6118
## cyl8 -3.1859 2.1705 -1.4678 0.1422 -7.4399 1.0682
## hp -0.0231 0.0120 -1.9344 0.0531 -0.0465 0.0003
## wt -3.1814 0.7196 -4.4211 0.0000 -4.5918 -1.7710. quietly reg mpg cyl c.hp##c.hp wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OLS
Expression : Linear prediction, predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | -.3696041 .6163571 -0.60 0.554 -1.634264 .8950561
hp | -.0429018 .0178353 -2.41 0.023 -.0794969 -.0063066
wt | -2.873553 .7301251 -3.94 0.001 -4.371646 -1.37546
------------------------------------------------------------------------------
x <- lm(mpg ~ cyl + hp + I(hp^2) + wt, data = mtcars)
summary(margins(x))## factor AME SE z p lower upper
## cyl -0.3696 0.6164 -0.5997 0.5487 -1.5776 0.8384
## hp -0.0429 0.0178 -2.4054 0.0162 -0.0779 -0.0079
## wt -2.8736 0.7301 -3.9357 0.0001 -4.3046 -1.4425. gen hp2 = hp^2
. quietly reg mpg cyl hp2 wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OLS
Expression : Linear prediction, predict()
dy/dx w.r.t. : cyl hp2 wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | -1.21919 .5030753 -2.42 0.022 -2.249693 -.1886869
hp2 | -.000028 .0000276 -1.01 0.320 -.0000846 .0000286
wt | -3.218637 .7570747 -4.25 0.000 -4.769435 -1.66784
------------------------------------------------------------------------------x <- lm(mpg ~ cyl + I(hp^2) + wt, data = mtcars)
summary(margins(x))## factor AME SE z p lower upper
## cyl -1.2192 0.5031 -2.4235 0.0154 -2.2052 -0.2332
## hp -0.0082 0.0081 -1.0124 0.3114 -0.0241 0.0077
## wt -3.2186 0.7571 -4.2514 0.0000 -4.7025 -1.7348. quietly logit am cyl hp wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Pr(am), predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | .0214527 .0469746 0.46 0.648 -.0706157 .1135212
hp | .0014339 .0006182 2.32 0.020 .0002224 .0026455
wt | -.4025475 .1154098 -3.49 0.000 -.6287466 -.1763484
------------------------------------------------------------------------------
. quietly logit am cyl hp wt
. margins, dydx(*) predict(xb)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Linear prediction (log odds), predict(xb)
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | .4875978 1.071621 0.46 0.649 -1.612741 2.587936
hp | .0325917 .0188611 1.73 0.084 -.0043753 .0695587
wt | -9.14947 4.153326 -2.20 0.028 -17.28984 -1.009101
------------------------------------------------------------------------------x <- glm(am ~ cyl + hp + wt, data = mtcars, family = binomial)
# AME
summary(margins(x, type = "response"))## factor AME SE z p lower upper
## cyl 0.0215 0.0470 0.4567 0.6479 -0.0706 0.1135
## hp 0.0014 0.0006 2.3197 0.0204 0.0002 0.0026
## wt -0.4025 0.1154 -3.4880 0.0005 -0.6287 -0.1764# AME and MEM equivalent on "link" scale
summary(margins(x, type = "link"))## factor AME SE z p lower upper
## cyl 0.4876 1.0716 0.4550 0.6491 -1.6127 2.5879
## hp 0.0326 0.0189 1.7280 0.0840 -0.0044 0.0696
## wt -9.1495 4.1533 -2.2029 0.0276 -17.2898 -1.0091. quietly logit am i.cyl hp wt
. margins, dydx(*) predict(xb)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Linear prediction (log odds), predict(xb)
dy/dx w.r.t. : 6.cyl 8.cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl |
6 | 2.765754 3.156829 0.88 0.381 -3.421517 8.953025
8 | -8.388958 13.16745 -0.64 0.524 -34.1967 17.41878
|
hp | .103209 .0960655 1.07 0.283 -.0850759 .2914939
wt | -10.67598 5.441998 -1.96 0.050 -21.3421 -.0098575
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Pr(am), predict()
dy/dx w.r.t. : 6.cyl 8.cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl |
6 | .1197978 .1062873 1.13 0.260 -.0885214 .3281171
8 | -.3478575 .2067542 -1.68 0.092 -.7530883 .0573732
|
hp | .0033268 .0029852 1.11 0.265 -.0025241 .0091777
wt | -.3441297 .1188604 -2.90 0.004 -.5770919 -.1111675
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.x <- glm(am ~ factor(cyl) + hp + wt, data = mtcars, family = binomial)
# Log-odds
summary(margins(x, type = "link"))## factor AME SE z p lower upper
## cyl6 2.7658 3.1568 0.8761 0.3810 -3.4214 8.9529
## cyl8 -8.3890 13.1671 -0.6371 0.5240 -34.1960 17.4181
## hp 0.1032 0.0961 1.0744 0.2826 -0.0851 0.2915
## wt -10.6760 5.4418 -1.9618 0.0498 -21.3418 -0.0102# Probability with continuous factors
summary(margins(x, type = "response"))## factor AME SE z p lower upper
## cyl6 0.1198 0.1063 1.1271 0.2597 -0.0885 0.3281
## cyl8 -0.3479 0.2068 -1.6825 0.0925 -0.7531 0.0574
## hp 0.0033 0.0030 1.1144 0.2651 -0.0025 0.0092
## wt -0.3441 0.1189 -2.8953 0.0038 -0.5771 -0.1112. quietly logit am cyl c.hp##c.wt
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Pr(am), predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | .0215633 .0492676 0.44 0.662 -.0749994 .1181261
hp | .0026673 .0023004 1.16 0.246 -.0018414 .007176
wt | -.5157922 .2685806 -1.92 0.055 -1.042201 .0106162
------------------------------------------------------------------------------
. margins, dydx(*) predict(xb)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Linear prediction (log odds), predict(xb)
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | .5156396 1.169458 0.44 0.659 -1.776456 2.807735
hp | .0515116 .035699 1.44 0.149 -.0184571 .1214804
wt | -12.24264 7.678428 -1.59 0.111 -27.29208 2.806807
------------------------------------------------------------------------------x <- glm(am ~ cyl + hp * wt, data = mtcars, family = binomial)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# AME
summary(margins(x, type = "response"))## factor AME SE z p lower upper
## cyl 0.0216 0.0493 0.4377 0.6616 -0.0750 0.1181
## hp 0.0027 0.0023 1.1595 0.2462 -0.0018 0.0072
## wt -0.5158 0.2685 -1.9209 0.0547 -1.0421 0.0105# AME and MEM equivalent on "link" scale
summary(margins(x, type = "link"))## factor AME SE z p lower upper
## cyl 0.5156 1.1694 0.4409 0.6593 -1.7764 2.8077
## hp 0.0515 0.0357 1.4429 0.1490 -0.0185 0.1215
## wt -12.2426 7.6784 -1.5944 0.1108 -27.2920 2.8067. quietly probit am cyl c.hp##c.wt
. margins, dydx(*) predict(xb)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Linear prediction, predict(xb)
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | .2974758 .6629205 0.45 0.654 -1.001825 1.596776
hp | .0277713 .0193121 1.44 0.150 -.0100797 .0656223
wt | -6.626949 4.096208 -1.62 0.106 -14.65537 1.401471
------------------------------------------------------------------------------
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Pr(am), predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | .022611 .0498253 0.45 0.650 -.0750447 .1202667
hp | .0025769 .0022607 1.14 0.254 -.001854 .0070077
wt | -.508829 .2625404 -1.94 0.053 -1.023399 .0057408
------------------------------------------------------------------------------x <- glm(am ~ cyl + hp * wt, data = mtcars, family = binomial(link="probit"))## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# AME (log-odds)
summary(margins(x, type = "link"))## factor AME SE z p lower upper
## cyl 0.2975 0.6472 0.4596 0.6458 -0.9710 1.5660
## hp 0.0278 0.0184 1.5075 0.1317 -0.0083 0.0639
## wt -6.6269 3.9095 -1.6951 0.0901 -14.2894 1.0355# AME (probability)
summary(margins(x, type = "response"))## factor AME SE z p lower upper
## cyl 0.0226 0.0492 0.4598 0.6456 -0.0738 0.1190
## hp 0.0026 0.0021 1.2244 0.2208 -0.0015 0.0067
## wt -0.5088 0.2479 -2.0523 0.0401 -0.9948 -0.0229. quietly poisson carb cyl c.hp##c.wt
. margins, dydx(*) predict(xb)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Linear prediction, predict(xb)
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | -.0993854 .1478936 -0.67 0.502 -.3892516 .1904808
hp | .0066519 .0024217 2.75 0.006 .0019054 .0113984
wt | .1225051 .2035185 0.60 0.547 -.2763837 .521394
------------------------------------------------------------------------------
. margins, dydx(*)
Average marginal effects Number of obs = 32
Model VCE : OIM
Expression : Predicted number of events, predict()
dy/dx w.r.t. : cyl hp wt
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cyl | -.2795214 .4169931 -0.67 0.503 -1.096813 .53777
hp | .0175935 .0067179 2.62 0.009 .0044267 .0307604
wt | .2075447 .4859868 0.43 0.669 -.7449719 1.160061
------------------------------------------------------------------------------x <- glm(carb ~ cyl + hp * wt, data = mtcars, family = poisson)
# AME (linear/link)
summary(margins(x, type = "link"))## factor AME SE z p lower upper
## cyl -0.0994 0.1479 -0.6720 0.5016 -0.3893 0.1905
## hp 0.0067 0.0024 2.7468 0.0060 0.0019 0.0114
## wt 0.1225 0.2035 0.6019 0.5472 -0.2764 0.5214# AME (probability)
summary(margins(x, type = "response"))## factor AME SE z p lower upper
## cyl -0.2795 0.4170 -0.6703 0.5026 -1.0968 0.5378
## hp 0.0176 0.0067 2.6189 0.0088 0.0044 0.0308
## wt 0.2075 0.4860 0.4271 0.6693 -0.7449 1.1600These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.