I am trying to explain some coefficients from a GLM regression with family(gamma) link(log), I did read that for coefficients I need transform to a exp(b1). But I am more interesting in dy/dx results. I need to transforms this results too?, some example of my results are:
Code:
. sysuse auto
(1978 Automobile Data)
.
. glm mpg weight length displacement , family(gamma) link(log)
Iteration 0: log likelihood = -298.5288
Iteration 1: log likelihood = -298.52698
Iteration 2: log likelihood = -298.52698
Generalized linear models No. of obs = 74
Optimization : ML Residual df = 70
Scale parameter = .0214501
Deviance = 1.422701405 (1/df) Deviance = .0203243
Pearson = 1.501510196 (1/df) Pearson = .0214501
Variance function: V(u) = u^2 [Gamma]
Link function : g(u) = ln(u) [Log]
AIC = 8.176405
Log likelihood = -298.5269823 BIC = -299.8619
------------------------------------------------------------------------------
| OIM
mpg | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | -.0001956 .0000818 -2.39 0.017 -.0003559 -.0000352
length | -.0033186 .002328 -1.43 0.154 -.0078814 .0012441
displacement | 5.40e-06 .0004179 0.01 0.990 -.0008136 .0008245
_cons | 4.24724 .2591944 16.39 0.000 3.739228 4.755251
------------------------------------------------------------------------------
.
. margins, dydx(*)
Average marginal effects Number of obs = 74
Model VCE : OIM
Expression : Predicted mean mpg, predict()
dy/dx w.r.t. : weight length displacement
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | -.0041641 .0017465 -2.38 0.017 -.0075871 -.000741
length | -.0706644 .0496204 -1.42 0.154 -.1679186 .0265898
displacement | .000115 .0088983 0.01 0.990 -.0173252 .0175553
------------------------------------------------------------------------------
Thanks In advance
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