handout - Daina Chiba

3.2 Calculating Substantive Effects (a.k.a., CLARIFY “by hand”)
Suppose you want to calculate substantive effects of some variable after running some fancy
statistical model, but CLARIFY does not support the model. In this case, you have to
implement the CLARIFY procedure “by hand.” I believe Randy has already covered how to
do it with Stata, so I will show you how to do it with R.
Assume further that the model you want to estimate is not readily available in R. For
example, models such as Heckman’s probit, Sartori’s selection model, von Stein’s selection
model, and censored duration model by Boehmke et al. can be easily estimated on Stata
with ado file, but estimating these models in R requires a bit of programming. 2
1. Estimate a model in Stata
2. Save the estiamtes (coefficient vector and variance covariance matrix) and export them
as a text file
3. From R, import the estimates
4. Simulate coefficients ( ˜ β)
5. Set X of your interest at some value while holding the other variables at mean or
median.
6. Calculate E(Y) = F (X ˜ β), where F (·) is some link function (probit, logit, etc.)
7. Repeat the steps 5 and 6
As we have already completed the steps 1 & 2 in Section 2.3, we will begin with 3.
A nice thing about using R is that we don’t really need to iterate the Steps 5 and 6
using loop. Instead, we will use matrix to set X at many values and calculate E(Y) in one
iteration.
Step 3: Importing estimates from Stata
Run the following chunk of R code (in 2_RPart.R).
R code
> beta vcov std.err z.score p.value reg.tbl colnames(reg.tbl) reg.tbl
Compare the table shown in your R window and the one in your Stata window. They should
be identical.
2 In the future, you might want to learn how to program these models in R, but for now, let’s assume
that we don’t have time to do it.
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