How to replicate Stata's "margins at" in R after lm() - r

Question

From Stata:
margins, at(age=40)
To understand why that yields the desired result, let us tell you that if you were to type
. margins
margins would report the overall margin—the margin that holds nothing constant. Because our model
is logistic, the average value of the predicted probabilities would be reported. The at() option fixes
one or more covariates to the value(s) specified and can be used with both factor and continuous
variables. Thus, if you typed
margins, at(age=40)
then margins would average over the data
the responses for everybody, setting age=40.
Could someone help me which package could be useful? I tried already to find a mean of predicted values for the subset data, but it doesnt work for sequences, for example margins, at(age=40 (1)50).

Answer 1

There are many ways to get marginal effects in R.
You should understand that Stata's margins, at are simply marginal effects evaluated at means or representative points (see this and the documentation).
I think that you'll like this solution best as it's most similar to what you're used to:
library(devtools)
install_github("leeper/margins")
Source: https://github.com/leeper/margins
margins is an effort to port Stata's (closed source) margins command
to R as an S3 generic method for calculating the marginal effects (or
"partial effects") of covariates included in model objects (like those
of classes "lm" and "glm"). A plot method for the new "margins" class
additionally ports the marginsplot command.
library(margins)
x <- lm(mpg ~ cyl * hp + wt, data = mtcars)
(m <- margins(x))
cyl hp wt
0.03814 -0.04632 -3.11981
See also the prediction command (?prediction) in this package.
Asides from that, here are some other solutions I've compiled:
I. erer (package)
maBina() command
http://cran.r-project.org/web/packages/erer/erer.pdf
II. mfxboot
mfxboot <- function(modform,dist,data,boot=1000,digits=3){
x <- glm(modform, family=binomial(link=dist),data)
# get marginal effects
pdf <- ifelse(dist=="probit",
mean(dnorm(predict(x, type = "link"))),
mean(dlogis(predict(x, type = "link"))))
marginal.effects <- pdf*coef(x)
# start bootstrap
bootvals <- matrix(rep(NA,boot*length(coef(x))), nrow=boot)
set.seed(1111)
for(i in 1:boot){
samp1 <- data[sample(1:dim(data)[1],replace=T,dim(data)[1]),]
x1 <- glm(modform, family=binomial(link=dist),samp1)
pdf1 <- ifelse(dist=="probit",
mean(dnorm(predict(x, type = "link"))),
mean(dlogis(predict(x, type = "link"))))
bootvals[i,] <- pdf1*coef(x1)
}
res <- cbind(marginal.effects,apply(bootvals,2,sd),marginal.effects/apply(bootvals,2,sd))
if(names(x$coefficients[1])=="(Intercept)"){
res1 <- res[2:nrow(res),]
res2 <- matrix(as.numeric(sprintf(paste("%.",paste(digits,"f",sep=""),sep=""),res1)),nrow=dim(res1)[1])
rownames(res2) <- rownames(res1)
} else {
res2 <- matrix(as.numeric(sprintf(paste("%.",paste(digits,"f",sep=""),sep="")),nrow=dim(res)[1]))
rownames(res2) <- rownames(res)
}
colnames(res2) <- c("marginal.effect","standard.error","z.ratio")
return(res2)}
Source: http://www.r-bloggers.com/probitlogit-marginal-effects-in-r/
III. Source: R probit regression marginal effects
x1 = rbinom(100,1,.5)
x2 = rbinom(100,1,.3)
x3 = rbinom(100,1,.9)
ystar = -.5 + x1 + x2 - x3 + rnorm(100)
y = ifelse(ystar>0,1,0)
probit = glm(y~x1 + x2 + x3, family=binomial(link='probit'))
xbar <- as.matrix(mean(cbind(1,ttt[1:3])))
Now the graphic, i.e., the marginal effect of x1, x2 and x3
library(arm)
curve(invlogit(1.6*(probit$coef[1] + probit$coef[2]*x + probit$coef[3]*xbar[3] + probit$coef[4]*xbar[4]))) #x1
curve(invlogit(1.6*(probit$coef[1] + probit$coef[2]*xbar[2] + probit$coef[3]*x + probit$coef[4]*xbar[4]))) #x2
curve(invlogit(1.6*(probit$coef[1] + probit$coef[2]*xbar[2] + probit$coef[3]*xbar[3] + probit$coef[4]*x))) #x3
library(AER)
data(SwissLabor)
mfx1 <- mfxboot(participation ~ . + I(age^2),"probit",SwissLabor)
mfx2 <- mfxboot(participation ~ . + I(age^2),"logit",SwissLabor)
mfx3 <- mfxboot(participation ~ . + I(age^2),"probit",SwissLabor,boot=100,digits=4)
mfxdat <- data.frame(cbind(rownames(mfx1),mfx1))
mfxdat$me <- as.numeric(as.character(mfxdat$marginal.effect))
mfxdat$se <- as.numeric(as.character(mfxdat$standard.error))
# coefplot
library(ggplot2)
ggplot(mfxdat, aes(V1, marginal.effect,ymin = me - 2*se,ymax= me + 2*se)) +
scale_x_discrete('Variable') +
scale_y_continuous('Marginal Effect',limits=c(-0.5,1)) +
theme_bw() +
geom_errorbar(aes(x = V1, y = me),size=.3,width=.2) +
geom_point(aes(x = V1, y = me)) +
geom_hline(yintercept=0) +
coord_flip() +
opts(title="Marginal Effects with 95% Confidence Intervals")