In leeper/margins: Marginal Effects for Model Objects
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)
OLS marginal effects
Stata
. 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
------------------------------------------------------------------------------
R
library("margins")
x <- lm(mpg ~ cyl + hp + wt, data = mtcars)
summary(margins(x))
OLS marginal effects with interaction
Stata
. 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
------------------------------------------------------------------------------
R
x <- lm(mpg ~ cyl + hp * wt, data = mtcars)
summary(margins(x))
OLS marginal effects with factor term
Stata
. 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.
R
x <- lm(mpg ~ factor(cyl) + hp + wt, data = mtcars)
summary(margins(x))
OLS marginal effects with squared term
Stata
. 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
------------------------------------------------------------------------------
R
x <- lm(mpg ~ cyl + hp + I(hp^2) + wt, data = mtcars)
summary(margins(x))
OLS marginal effects with squared term (but no first-order term)
Stata
. 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
------------------------------------------------------------------------------
R
x <- lm(mpg ~ cyl + I(hp^2) + wt, data = mtcars)
summary(margins(x))
Logit effects on log-odds and probability scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(am ~ cyl + hp + wt, data = mtcars, family = binomial)
# AME
summary(margins(x, type = "response"))
# AME and MEM equivalent on "link" scale
summary(margins(x, type = "link"))
Logit effects with factor variable on log-odds and probability scales
Stata
. 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.
R
x <- glm(am ~ factor(cyl) + hp + wt, data = mtcars, family = binomial)
# Log-odds
summary(margins(x, type = "link"))
# Probability with continuous factors
summary(margins(x, type = "response"))
Logit with interaction on probability and Log-Odds scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(am ~ cyl + hp * wt, data = mtcars, family = binomial)
# AME
summary(margins(x, type = "response"))
# AME and MEM equivalent on "link" scale
summary(margins(x, type = "link"))
Probit effects on latent and probability scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(am ~ cyl + hp * wt, data = mtcars, family = binomial(link="probit"))
# AME (log-odds)
summary(margins(x, type = "link"))
# AME (probability)
summary(margins(x, type = "response"))
Poisson effects on latent and probability scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(carb ~ cyl + hp * wt, data = mtcars, family = poisson)
# AME (linear/link)
summary(margins(x, type = "link"))
# AME (probability)
summary(margins(x, type = "response"))
leeper/margins documentation built on Jan. 26, 2021, 9:12 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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)
OLS marginal effects
Stata
. 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
------------------------------------------------------------------------------
R
library("margins") x <- lm(mpg ~ cyl + hp + wt, data = mtcars) summary(margins(x))
OLS marginal effects with interaction
Stata
. 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
------------------------------------------------------------------------------
R
x <- lm(mpg ~ cyl + hp * wt, data = mtcars) summary(margins(x))
OLS marginal effects with factor term
Stata
. 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.
R
x <- lm(mpg ~ factor(cyl) + hp + wt, data = mtcars) summary(margins(x))
OLS marginal effects with squared term
Stata
. 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
------------------------------------------------------------------------------
R
x <- lm(mpg ~ cyl + hp + I(hp^2) + wt, data = mtcars) summary(margins(x))
OLS marginal effects with squared term (but no first-order term)
Stata
. 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
------------------------------------------------------------------------------
R
x <- lm(mpg ~ cyl + I(hp^2) + wt, data = mtcars) summary(margins(x))
Logit effects on log-odds and probability scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(am ~ cyl + hp + wt, data = mtcars, family = binomial) # AME summary(margins(x, type = "response")) # AME and MEM equivalent on "link" scale summary(margins(x, type = "link"))
Logit effects with factor variable on log-odds and probability scales
Stata
. 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.
R
x <- glm(am ~ factor(cyl) + hp + wt, data = mtcars, family = binomial) # Log-odds summary(margins(x, type = "link")) # Probability with continuous factors summary(margins(x, type = "response"))
Logit with interaction on probability and Log-Odds scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(am ~ cyl + hp * wt, data = mtcars, family = binomial) # AME summary(margins(x, type = "response")) # AME and MEM equivalent on "link" scale summary(margins(x, type = "link"))
Probit effects on latent and probability scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(am ~ cyl + hp * wt, data = mtcars, family = binomial(link="probit")) # AME (log-odds) summary(margins(x, type = "link")) # AME (probability) summary(margins(x, type = "response"))
Poisson effects on latent and probability scales
Stata
. 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
------------------------------------------------------------------------------
R
x <- glm(carb ~ cyl + hp * wt, data = mtcars, family = poisson) # AME (linear/link) summary(margins(x, type = "link")) # AME (probability) summary(margins(x, type = "response"))
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