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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