This post translates the Stata code to calculate the standard error of the predictions contained in the document, How are average marginal effects and their standard errors computed by margins using the delta method? by Jeff Pitblado of StataCorp, into R code.

R presents more of a challenge to Stata on many fronts, one of which is basic data management.

I often find myself calculating the value of one observation given the value of an adjacent value. For example, to assess a lagged effect, I would take the value of the preceding interval. Stata makes this really easy, R not so much.

Here's what we would do in Stata:

set obs 1000
gen i = _n
gen val = round(runiform()*10)
gen lag = val[_n-1]

The last command throws the warning, (1 missing value generated) because the first observation has no lagged observation. The first 10 observations ...

I am attempting to learn R. This is either a great thing or a terrible, terrible mistake while on the tenure clock. But, all the cool kids are doing -- so even though they might also jump off a bridge, I'm going to jump into R.

The hardest part so far is doing things that now come as second nature to me in Stata. Although R's tools are much better in the long-run, learning what types of objects different functions return and such ends up being a very high learning curve.

A while back (all posts are a while back now), I wrote a post describing how to import ...