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A tutorial on tidy cross-validation with R Analyzing NetHack data, part 1: What kills the players Analyzing NetHack data, part 2: What players kill the most Building a shiny app to explore historical newspapers: a step-by-step guide Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1 Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2 Curly-Curly, the successor of Bang-Bang Dealing with heteroskedasticity; regression with robust standard errors using R Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer Fast food, causality and R packages, part 1 Fast food, causality and R packages, part 2 For posterity: install {xml2} on GNU/Linux distros Forecasting my weight with R From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick} Getting data from pdfs using the pdftools package Getting the data from the Luxembourguish elections out of Excel Going from a human readable Excel file to a machine-readable csv with {tidyxl} Historical newspaper scraping with {tesseract} and R How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data Imputing missing values in parallel using {furrr} Intermittent demand, Croston and Die Hard Looking into 19th century ads from a Luxembourguish newspaper with R Making sense of the METS and ALTO XML standards Manipulate dates easily with {lubridate} Manipulating strings with the {stringr} package Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data Missing data imputation and instrumental variables regression: the tidy approach Modern R with the tidyverse is available on Leanpub Objects types and some useful R functions for beginners Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()` R or Python? Why not both? Using Anaconda Python within R with {reticulate} Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach Some fun with {gganimate} Split-apply-combine for Maximum Likelihood Estimation of a linear model Statistical matching, or when one single data source is not enough The best way to visit Luxembourguish castles is doing data science + combinatorial optimization The never-ending editor war (?) 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Easy peasy STATA-like marginal effects with R

Model interpretation is essential in the social sciences. If one wants to know the effect of variable x on the dependent variable y, marginal effects are an easy way to get the answer. STATA includes a margins command that has been ported to R by Thomas J. Leeper of the London School of Economics and Political Science. You can find the source code of the package on github. In this short blog post, I demo some of the functionality of margins.

First, let’s load some packages:


As an example, we are going to use the Participation data from the Ecdat package:

Labor Force Participation


a cross-section

number of observations : 872

observation : individuals

country : Switzerland



A dataframe containing :

labour force participation ?

the log of nonlabour income

age in years divided by 10

years of formal education

the number of young children (younger than 7)

number of older children

foreigner ?


Gerfin, Michael (1996) “Parametric and semiparametric estimation of the binary response”, Journal of Applied Econometrics, 11(3), 321-340.


Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, http://www.econ.queensu.ca/ETM/, chapter 11.

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

The variable of interest is lfp: whether the individual participates in the labour force or not. To know which variables are relevant in the decision to participate in the labour force, one could estimate a logit model, using glm().

logit_participation = glm(lfp ~ ., data = Participation, family = "binomial")

Now that we ran the regression, we can take a look at the results. I like to use broom::tidy() to look at the results of regressions, as tidy() returns a nice data.frame, but you could use summary() if you’re only interested in reading the output:

##          term    estimate  std.error  statistic      p.value
## 1 (Intercept) 10.37434616 2.16685216  4.7877499 1.686617e-06
## 2     lnnlinc -0.81504064 0.20550116 -3.9661122 7.305449e-05
## 3         age -0.51032975 0.09051783 -5.6378920 1.721444e-08
## 4        educ  0.03172803 0.02903580  1.0927211 2.745163e-01
## 5         nyc -1.33072362 0.18017027 -7.3859224 1.514000e-13
## 6         noc -0.02198573 0.07376636 -0.2980454 7.656685e-01
## 7  foreignyes  1.31040497 0.19975784  6.5599678 5.381941e-11

From the results above, one can only interpret the sign of the coefficients. To know how much a variable influences the labour force participation, one has to use margins():

effects_logit_participation = margins(logit_participation) 

## Average marginal effects
## glm(formula = lfp ~ ., family = "binomial", data = Participation)
##  lnnlinc     age     educ     nyc       noc foreignyes
##  -0.1699 -0.1064 0.006616 -0.2775 -0.004584     0.2834

Using summary() on the object returned by margins() provides more details:

##      factor     AME     SE       z      p   lower   upper
##         age -0.1064 0.0176 -6.0494 0.0000 -0.1409 -0.0719
##        educ  0.0066 0.0060  1.0955 0.2733 -0.0052  0.0185
##  foreignyes  0.2834 0.0399  7.1102 0.0000  0.2053  0.3615
##     lnnlinc -0.1699 0.0415 -4.0994 0.0000 -0.2512 -0.0887
##         noc -0.0046 0.0154 -0.2981 0.7656 -0.0347  0.0256
##         nyc -0.2775 0.0333 -8.3433 0.0000 -0.3426 -0.2123

And it is also possible to plot the effects with base graphics:


This uses the basic R plotting capabilities, which is useful because it is a simple call to the function plot() but if you’ve been using ggplot2 and want this graph to have the same look as the others made with ggplot2 you first need to save the summary in a variable. Let’s overwrite this effects_logit_participation variable with its summary:

effects_logit_participation = summary(effects_logit_participation)

And now it is possible to use ggplot2 to create the same plot:

ggplot(data = effects_logit_participation) +
  geom_point(aes(factor, AME)) +
  geom_errorbar(aes(x = factor, ymin = lower, ymax = upper)) +
  geom_hline(yintercept = 0) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45))

So an infinitesimal increase, in say, non-labour income (lnnlinc) of 0.001 is associated with a decrease of the probability of labour force participation by 0.001*17 percentage points.

You can also extract the marginal effects of a single variable, with dydx():

head(dydx(Participation, logit_participation, "lnnlinc"))
##   dydx_lnnlinc
## 1  -0.15667764
## 2  -0.20014487
## 3  -0.18495109
## 4  -0.05377262
## 5  -0.18710476
## 6  -0.19586986

Which makes it possible to extract the effects for a list of individuals that you can create yourself:

my_subjects = tribble(
    ~lfp,  ~lnnlinc, ~age, ~educ, ~nyc, ~noc, ~foreign,
    "yes",   10.780,  7.0,     4,    1,    1,    "yes",
     "no",     1.30,  9.0,     1,    4,    1,    "yes"

dydx(my_subjects, logit_participation, "lnnlinc")
##   dydx_lnnlinc
## 1  -0.09228119
## 2  -0.17953451

I used the tribble() function from the tibble package to create this test data set, row by row. Then, using dydx(), I get the marginal effect of variable lnnlinc for these two individuals. No doubt that this package will be a huge help convincing more social scientists to try out R and make a potential transition from STATA easier.