Somebody might have done this, but in fact it's not difficult to compute the marginal effects yourself (which is the beauty of R). For a univariate logistic regression, I illustrate two ways to compute the marginal effects (one corresponds to the mfx, the other one to the margeff command in Stata). With the first you compute the marginal effect based on the mean fitted values; with the second you compute the marginal effect based on the fitted values for each observation and then mean over the individual marginal effects. Often the second way is considered better. You can easily extend the R-code below to a multivariate regression.
##### #####Simulate data and run regression ##### set.seed(343) x=rnorm(100,0,1) #linear predictor lp=exp(x)/(1+exp(x)) #probability y=rbinom(100,1,lp) #Bernoulli draws with probability lp #Run logistic regression reg=glm(y~x,binomial) summary(reg) ##### #####Regression output ##### Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.1921 0.2175 0.883 0.377133 x 0.9442 0.2824 3.343 0.000829 *** --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 138.47 on 99 degrees of freedom Residual deviance: 125.01 on 98 degrees of freedom AIC: 129.01 ##### #####Compute marginal effects ##### #Way 1 mean(fitted(reg))*mean(1-fitted(reg))*coefficients(reg)[2] 0.2356697 #Way 2 mean(fitted(reg)*(1-fitted(reg))*coefficients(reg)[2]) 0.2057041 ##### #####Check with Stata ##### Logistic regression Number of obs = 100 LR chi2(1) = 13.46 Prob > chi2 = 0.0002 Log likelihood = -62.506426 Pseudo R2 = 0.0972 ---------------------------------------------------------------------------- -- y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------- -- x | .9441896 .2824403 3.34 0.001 .3906167 1.497762 _cons | .1920529 .2174531 0.88 0.377 -.2341474 .6182532 ---------------------------------------------------------------------------- -- ##### #####Compute marginal effects in Stata ##### #Way 1 Marginal effects after logit y = Pr(y) (predict) = .52354297 ---------------------------------------------------------------------------- -- variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+------------------------------------------------------------------ -- x | .2355241 .07041 3.35 0.001 .097532 .373516 -.103593 ---------------------------------------------------------------------------- -- #Way 2 Average marginal effects on Prob(y==1) after logit ---------------------------------------------------------------------------- -- y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------- -- x | .2057041 .0473328 4.35 0.000 .1129334 .2984747 ---------------------------------------------------------------------------- -- HTH, Daniel ------------------------- cuncta stricte discussurus ------------------------- -----Ursprüngliche Nachricht----- Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im Auftrag von Roberto Patuelli Gesendet: Monday, November 09, 2009 12:04 PM An: r-help@r-project.org Betreff: [R] Percentage effects in logistic regression Dear ALL, I'm trying to figure out what the percentage effects are in a logistic regression. To be more clear, I'm not interested in the effect on y of a 1-unit increase in x, but on the percentage effect on y of a 1% increase in x (in economics this is also often called an "elasticity"). For example, if my independent variables are in logs, the betas can be directly interpreted as percentage effects both in OLS and Poisson regression. What about the logistic regression? Is there a package (maybe effects?) that can compute these automatically? Thanks and best regards, Roberto Patuelli ******************** Roberto Patuelli, Ph.D. Istituto Ricerche Economiche (IRE) (Institute for Economic Research) Università della Svizzera Italiana (University of Lugano) via Maderno 24, CP 4361 CH-6904 Lugano Switzerland Phone: +41-(0)58-666-4166 Fax: +39-02-700419665 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.