36  Alternative Software

If you do not like marginaleffects, you may want to consider one of the alternatives described below:

• margins: https://cran.r-project.org/web/packages/margins/index.html
• prediction: https://cran.r-project.org/web/packages/prediction/index.html
• emmeans: https://cran.r-project.org/web/packages/emmeans/index.html
• brmsmargins: https://joshuawiley.com/brmsmargins/
• effects: https://cran.r-project.org/package=effects
• modelbased: https://easystats.github.io/modelbased/
• ggeffects: https://strengejacke.github.io/ggeffects/
• Stata by StataCorp LLC

36.1 emmeans

The emmeans package is developed by Russell V. Lenth and colleagues. emmeans is a truly incredible piece of software, and a trailblazer in the R ecosystem. It is an extremely powerful package whose functionality overlaps marginaleffects to a significant degree: marginal means, contrasts, and slopes. Even if the two packages can compute many of the same quantities, emmeans and marginaleffects have pretty different philosophies with respect to user interface and computation.

An emmeans analysis typically starts by computing “marginal means” by holding all numeric covariates at their means, and by averaging across a balanced grid of categorical predictors. Then, users can use the contrast() function to estimate the difference between marginal means.

The marginaleffects package supplies a predictions function which can replicate most emmeans analyses by computing marginal means. However, the typical analysis is more squarely centered on predicted/fitted values. This is a useful starting point because, in many cases, analysts will find it easy and intuitive to express their scientific queries in terms of changes in predicted values. For example,

• How does the average predicted probability of survival differ between treatment and control group?
• What is the difference between the predicted wage of college and high school graduates?

Let’s say we estimate a linear regression model with two continuous regressors and a multiplicative interaction:

\[y = \beta_0 + \beta_1 x + \beta_2 z + \beta_3 x \cdot z + \varepsilon\]

In this model, the effect of \(x\) on \(y\) will depend on the value of covariate \(z\). Let’s say the user wants to estimate what happens to the predicted value of \(y\) when \(x\) increases by 1 unit, when \(z \in \{-1, 0, 1\}\). To do this, we use the comparisons() function. The variables argument determines the scientific query of interest, and the newdata argument determines the grid of covariate values on which we want to evaluate the query:

model <- lm(y ~ x * z, data)

comparisons(
  model,
  variables = list(x = 1), # what is the effect of 1-unit change in x?
  newdata = datagrid(z = -1:1) # when z is held at values -1, 0, or 1
)

As the vignettes show, marginaleffects can also compute contrasts on marginal means. It can also compute various quantities of interest like raw fitted values, slopes (partial derivatives), and contrasts between marginal means. It also offers a flexible mechanism to run (non-)linear hypothesis tests using the delta method, and it offers fully customizable strategy to compute quantities like odds ratios (or completely arbitrary functions of predicted outcome).

Thus, in my (Vincent’s) biased opinion, the main benefits of marginaleffects over emmeans are:

• Support more model types.
• Simpler, more intuitive, and highly consistent user interface.
• Easier to compute average slopes or unit-level contrasts for whole datasets.
• Easier to compute slopes (aka marginal effects, trends, or partial derivatives) for custom grids and continuous regressors.
• Easier to implement causal inference strategies like the parametric g-formula and regression adjustment in experiments (see vignettes).
• Allows the computation of arbitrary quantities of interest via user-supplied functions and automatic delta method inference.
• Common plots are easy with the plot_predictions(), plot_comparisons(), and plot_slopes() functions.

To be fair, many of the marginaleffects advantages listed above come down to subjective preferences over user interface. Readers are thus encouraged to try both packages to see which interface they prefer.

The main advantages of emmeans over marginaleffects arise when users are specifically interested in marginal means, where emmeans tends to be much faster and to have a lot of functionality to handle backtransformations. emmeans also has better functionality for effect sizes; notably, the eff_size() function can return effect size estimates that account for uncertainty in both estimated effects and the population SD.

Please let me know if you find other features in emmeans so I can add them to this list.

The Marginal Means Vignette includes side-by-side comparisons of emmeans and marginaleffects to compute marginal means. The rest of this section compares the syntax for contrasts and marginaleffects.

36.1.1 Contrasts

As far as I can tell, emmeans does not provide an easy way to compute unit-level contrasts for every row of the dataset used to fit our model. Therefore, the side-by-side syntax shown below will always include newdata=datagrid() to specify that we want to compute only one contrast: at the mean values of the regressors. In day-to-day practice with slopes(), however, this extra argument would not be necessary.

Fit a model:

library(emmeans)
library(marginaleffects)

mod <- glm(vs ~ hp + factor(cyl), data = mtcars, family = binomial)

Link scale, pairwise contrasts:

emm <- emmeans(mod, specs = "cyl")
contrast(emm, method = "revpairwise", adjust = "none", df = Inf)
#>  contrast    estimate      SE  df z.ratio p.value
#>  cyl6 - cyl4   -0.905    1.63 Inf  -0.555  0.5789
#>  cyl8 - cyl4  -19.542 4370.00 Inf  -0.004  0.9964
#>  cyl8 - cyl6  -18.637 4370.00 Inf  -0.004  0.9966
#> 
#> Degrees-of-freedom method: user-specified 
#> Results are given on the log odds ratio (not the response) scale.

comparisons(mod,
            type = "link",
            newdata = "mean",
            variables = list(cyl = "pairwise"))
#> 
#>  Contrast Estimate Std. Error        z Pr(>|z|)   S   2.5 %  97.5 %
#>     6 - 4   -0.905       1.63 -0.55506    0.579 0.8    -4.1    2.29
#>     8 - 4  -19.542    4367.17 -0.00447    0.996 0.0 -8579.0 8539.95
#>     8 - 6  -18.637    4367.17 -0.00427    0.997 0.0 -8578.1 8540.85
#> 
#> Term: cyl
#> Type: link

Response scale, reference groups:

emm <- emmeans(mod, specs = "cyl", regrid = "response")
contrast(emm, method = "trt.vs.ctrl1", adjust = "none", df = Inf, ratios = FALSE)
#>  contrast    estimate    SE  df z.ratio p.value
#>  cyl6 - cyl4   -0.222 0.394 Inf  -0.564  0.5727
#>  cyl8 - cyl4   -0.595 0.511 Inf  -1.163  0.2447
#> 
#> Degrees-of-freedom method: user-specified

comparisons(mod, newdata = "mean")
#> 
#>  Term Contrast  Estimate Std. Error         z Pr(>|z|)   S     2.5 %   97.5 %
#>   cyl    6 - 4 -2.22e-01   3.95e-01 -0.562539    0.574 0.8 -9.96e-01 5.52e-01
#>   cyl    8 - 4 -5.92e-01   5.14e-01 -1.151352    0.250 2.0 -1.60e+00 4.16e-01
#>   hp     +1    -1.52e-10   6.63e-07 -0.000229    1.000 0.0 -1.30e-06 1.30e-06
#> 
#> Type: response

36.1.2 Contrasts by group

Here is a slightly more complicated example with contrasts estimated by subgroup in a lme4 mixed effects model. First we estimate a model and compute pairwise contrasts by subgroup using emmeans:

library(dplyr)
library(lme4)
library(emmeans)

dat <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/lme4/VerbAgg.csv")
dat$woman <- as.numeric(dat$Gender == "F")

mod <- glmer(
    woman ~ btype * resp + situ + (1 + Anger | item),
    family = binomial,
    data = dat)

emmeans(mod, specs = "btype", by = "resp") |>
    contrast(method = "revpairwise", adjust = "none")
#> resp = no:
#>  contrast      estimate     SE  df z.ratio p.value
#>  scold - curse  -0.0152 0.1100 Inf  -0.139  0.8898
#>  shout - curse  -0.2533 0.1020 Inf  -2.478  0.0132
#>  shout - scold  -0.2381 0.0886 Inf  -2.686  0.0072
#> 
#> resp = perhaps:
#>  contrast      estimate     SE  df z.ratio p.value
#>  scold - curse  -0.2393 0.1180 Inf  -2.031  0.0422
#>  shout - curse  -0.0834 0.1330 Inf  -0.627  0.5309
#>  shout - scold   0.1559 0.1360 Inf   1.148  0.2510
#> 
#> resp = yes:
#>  contrast      estimate     SE  df z.ratio p.value
#>  scold - curse   0.0391 0.1290 Inf   0.302  0.7624
#>  shout - curse   0.5802 0.1780 Inf   3.252  0.0011
#>  shout - scold   0.5411 0.1890 Inf   2.866  0.0042
#> 
#> Results are averaged over the levels of: situ 
#> Results are given on the log odds ratio (not the response) scale.

What did emmeans do to obtain these results? Roughly speaking:

1. Create a prediction grid with one cell for each combination of categorical predictors in the model, and all numeric variables held at their means.
2. Make adjusted predictions in each cell of the prediction grid.
3. Take the average of those predictions (marginal means) for each combination of btype (focal variable) and resp (group by variable).
4. Compute pairwise differences (contrasts) in marginal means across different levels of the focal variable btype.

In short, emmeans computes pairwise contrasts between marginal means, which are themselves averages of adjusted predictions. This is different from the default types of contrasts produced by comparisons(), which reports contrasts between adjusted predictions, without averaging across a pre-specified grid of predictors. What does comparisons() do instead?

Let newdata be a data frame supplied by the user (or the original data frame used to fit the model), then:

1. Create a new data frame called newdata2, which is identical to newdata except that the focal variable is incremented by one level.
2. Compute contrasts as the difference between adjusted predictions made on the two datasets:
   • predict(model, newdata = newdata2) - predict(model, newdata = newdata)

Although it is not idiomatic, we can use still use comparisons() to emulate the emmeans results. First, we create a prediction grid with one cell for each combination of categorical predictor in the model:

nd <- datagrid(
    model = mod,
    resp = dat$resp,
    situ = dat$situ,
    btype = dat$btype)
nrow(nd)
#> [1] 18

This grid has 18 rows, one for each combination of levels for the resp (3), situ (2), and btype (3) variables (3 * 2 * 3 = 18).

Then we compute pairwise contrasts over this grid:

cmp <- comparisons(mod,
    variables = list("btype" = "pairwise"),
    newdata = nd,
    type = "link")
nrow(cmp)
#> [1] 54

There are 3 pairwise contrasts, corresponding to the 3 pairwise comparisons possible between the 3 levels of the focal variable btype: scold-curse, shout-scold, shout-curse. The comparisons() function estimates those 3 contrasts for each row of newdata, so we get \(18 \times 3 = 54\) rows.

Finally, if we wanted contrasts averaged over each subgroup of the resp variable, we can use the avg_comparisons() function with the by argument:

avg_comparisons(mod,
    by = "resp",
    variables = list("btype" = "pairwise"),
    newdata = nd,
    type = "link")
#> 
#>       Contrast    resp Estimate Std. Error      z Pr(>|z|)   S  2.5 %   97.5 %
#>  scold - curse no       -0.0152     0.1097 -0.139  0.88977 0.2 -0.230  0.19975
#>  shout - curse no       -0.2533     0.1022 -2.478  0.01319 6.2 -0.454 -0.05299
#>  shout - scold no       -0.2381     0.0886 -2.686  0.00723 7.1 -0.412 -0.06435
#>  scold - curse perhaps  -0.2393     0.1178 -2.031  0.04222 4.6 -0.470 -0.00841
#>  shout - curse perhaps  -0.0834     0.1330 -0.627  0.53091 0.9 -0.344  0.17738
#>  shout - scold perhaps   0.1559     0.1358  1.148  0.25102 2.0 -0.110  0.42215
#>  scold - curse yes       0.0391     0.1292  0.302  0.76239 0.4 -0.214  0.29235
#>  shout - curse yes       0.5802     0.1784  3.252  0.00115 9.8  0.230  0.92987
#>  shout - scold yes       0.5411     0.1888  2.866  0.00416 7.9  0.171  0.91116
#> 
#> Term: btype
#> Type: link

These results are identical to those produced by emmeans (except for \(t\) vs. \(z\)).

36.1.3 Marginal Effects

As far as I can tell, emmeans::emtrends makes it easier to compute marginal effects for a few user-specified values than for large grids or for the full original dataset.

Response scale, user-specified values:

mod <- glm(vs ~ hp + factor(cyl), data = mtcars, family = binomial)

emtrends(mod, ~hp, "hp", regrid = "response", at = list(cyl = 4))
#>   hp hp.trend    SE  df asymp.LCL asymp.UCL
#>  147 -0.00786 0.011 Inf   -0.0294    0.0137
#> 
#> Confidence level used: 0.95

slopes(mod, newdata = datagrid(cyl = 4))
#> 
#>  Term Contrast cyl Estimate Std. Error      z Pr(>|z|)   S   2.5 % 97.5 %
#>   cyl    6 - 4   4 -0.22211      0.395 -0.563    0.574 0.8 -0.9960 0.5517
#>   cyl    8 - 4   4 -0.59223      0.514 -1.151    0.250 2.0 -1.6004 0.4159
#>   hp     dY/dX   4 -0.00787      0.011 -0.717    0.473 1.1 -0.0294 0.0136
#> 
#> Type: response

Link scale, user-specified values:

emtrends(mod, ~hp, "hp", at = list(cyl = 4))
#>   hp hp.trend     SE  df asymp.LCL asymp.UCL
#>  147  -0.0326 0.0339 Inf    -0.099    0.0338
#> 
#> Confidence level used: 0.95

slopes(mod, type = "link", newdata = datagrid(cyl = 4))
#> 
#>  Term Contrast cyl Estimate Std. Error        z Pr(>|z|)   S     2.5 %   97.5 %
#>   cyl    6 - 4   4  -0.9049   1.63e+00 -0.55506    0.579 0.8    -4.100 2.29e+00
#>   cyl    8 - 4   4 -19.5418   4.37e+03 -0.00447    0.996 0.0 -8579.030 8.54e+03
#>   hp     dY/dX   4  -0.0326   3.39e-02 -0.96144    0.336 1.6    -0.099 3.38e-02
#> 
#> Type: link

36.1.4 More examples

Here are a few more emmeans vs. marginaleffects comparisons:

## Example of examining a continuous x categorical interaction using emmeans and marginaleffects
## Authors: Cameron Patrick and Vincent Arel-Bundock

library(tidyverse)
library(emmeans)
library(marginaleffects)

## use the mtcars data, set up am as a factor
data(mtcars)
mc <- mtcars |> mutate(am = factor(am))

## fit a linear model to mpg with wt x am interaction
m <- lm(mpg ~ wt*am, data = mc)
summary(m)

## 1. means for each level of am at mean wt.
emmeans(m, "am")
predictions(m, newdata = datagrid(am = 0:1))

## 2. means for each level of am at wt = 2.5, 3, 3.5.
emmeans(m, c("am", "wt"), at = list(wt = c(2.5, 3, 3.5)))
predictions(m, newdata = datagrid(am = 0:1, wt = c(2.5, 3, 3.5))

## 3. means for wt = 2.5, 3, 3.5, averaged over levels of am (implicitly!).
emmeans(m, "wt", at = list(wt = c(2.5, 3, 3.5)))

## same thing, but the averaging is more explicit, using the `by` argument
predictions(
  m,
  newdata = datagrid(am = 0:1, wt = c(2.5, 3, 3.5)),
  by = "wt")

## 4. graphical version of 2.
emmip(m, am ~ wt, at = list(wt = c(2.5, 3, 3.5)), CIs = TRUE)
plot_predictions(m, condition = c("wt", "am"))

## 5. compare levels of am at specific values of wt.
## this is a bit ugly because the emmeans defaults for pairs() are silly.
## infer = TRUE: enable confidence intervals.
## adjust = "none": begone, Tukey.
## reverse = TRUE: contrasts as (later level) - (earlier level)
pairs(emmeans(m, "am", by = "wt", at = list(wt = c(2.5, 3, 3.5))),
      infer = TRUE, adjust = "none", reverse = TRUE)

comparisons(
  m,
  variables = "am",
  newdata = datagrid(wt = c(2.5, 3, 3.5)))

## 6. plot of pairswise comparisons
plot(pairs(emmeans(m, "am", by = "wt", at = list(wt = c(2.5, 3, 3.5))),
      infer = TRUE, adjust = "none", reverse = TRUE))

## Since `wt` is numeric, the default is to plot it as a continuous variable on
## the x-axis.  But not that this is the **exact same info** as in the emmeans plot.
plot_comparisons(m, variables = "am", condition = "wt")

## You of course customize everything, set draw=FALSE, and feed the raw data to feed to ggplot2
p <- plot_comparisons(
  m,
  variables = "am",
  condition = list(wt = c(2.5, 3, 3.5)),
  draw = FALSE)

ggplot(p, aes(y = wt, x = comparison, xmin = conf.low, xmax = conf.high)) +
  geom_pointrange()

## 7. slope of wt for each level of am
emtrends(m, "am", "wt")
slopes(m, newdata = datagrid(am = 0:1))

36.2 margins and prediction

The margins and prediction packages for R were designed by Thomas Leeper to emulate the behavior of the margins command from Stata. These packages are trailblazers and strongly influenced the development of marginaleffects. The main benefits of marginaleffects over these packages are:

• Support more model types
• Faster
• Memory efficient
• Plots using ggplot2 instead of Base R
• More extensive test suite
• Active development

The syntax of the two packages is very similar.

36.2.1 Average Marginal Effects

library(margins)
library(marginaleffects)

mod <- lm(mpg ~ cyl + hp + wt, data = mtcars)

mar <- margins(mod)
summary(mar)
#>  factor     AME     SE       z      p   lower   upper
#>     cyl -0.9416 0.5509 -1.7092 0.0874 -2.0214  0.1382
#>      hp -0.0180 0.0119 -1.5188 0.1288 -0.0413  0.0052
#>      wt -3.1670 0.7406 -4.2764 0.0000 -4.6185 -1.7155

mfx <- slopes(mod)

36.2.2 Individual-Level Marginal Effects

Marginal effects in a user-specified data frame:

head(data.frame(mar))
#>    mpg cyl disp  hp drat    wt  qsec vs am gear carb   fitted se.fitted   dydx_cyl    dydx_hp   dydx_wt Var_dydx_cyl  Var_dydx_hp Var_dydx_wt X_weights X_at_number
#> 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 22.82043 0.6876212 -0.9416168 -0.0180381 -3.166973    0.3035123 0.0001410453   0.5484513        NA           1
#> 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 22.01285 0.6056817 -0.9416168 -0.0180381 -3.166973    0.3035123 0.0001410453   0.5484513        NA           1
#> 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 25.96040 0.7349593 -0.9416168 -0.0180381 -3.166973    0.3035123 0.0001410453   0.5484513        NA           1
#> 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 20.93608 0.5800910 -0.9416168 -0.0180381 -3.166973    0.3035123 0.0001410453   0.5484513        NA           1
#> 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 17.16780 0.8322986 -0.9416168 -0.0180381 -3.166973    0.3035123 0.0001410453   0.5484513        NA           1
#> 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 20.25036 0.6638322 -0.9416168 -0.0180381 -3.166973    0.3035123 0.0001410453   0.5484513        NA           1

head(mfx)
#> 
#>  Estimate Std. Error     z Pr(>|z|)   S 2.5 % 97.5 %
#>    -0.942      0.552 -1.71   0.0879 3.5 -2.02  0.140
#>    -0.942      0.552 -1.71   0.0879 3.5 -2.02  0.140
#>    -0.942      0.552 -1.71   0.0879 3.5 -2.02  0.140
#>    -0.942      0.552 -1.71   0.0879 3.5 -2.02  0.140
#>    -0.942      0.551 -1.71   0.0873 3.5 -2.02  0.138
#>    -0.942      0.552 -1.71   0.0879 3.5 -2.02  0.140
#> 
#> Term: cyl
#> Type: response
#> Comparison: dY/dX
nd <- data.frame(cyl = 4, hp = 110, wt = 3)

36.2.3 Marginal Effects at the Mean

mar <- margins(mod, data = data.frame(prediction::mean_or_mode(mtcars)), unit_ses = TRUE)
data.frame(mar)
#>        mpg    cyl     disp       hp     drat      wt     qsec     vs      am   gear   carb   fitted se.fitted   dydx_cyl    dydx_hp   dydx_wt Var_dydx_cyl  Var_dydx_hp Var_dydx_wt SE_dydx_cyl SE_dydx_hp SE_dydx_wt X_weights X_at_number
#> 1 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125 20.09062 0.4439832 -0.9416168 -0.0180381 -3.166973    0.3035082 0.0001410453   0.5484409   0.5509157 0.01187625  0.7405679        NA           1

slopes(mod, newdata = "mean")
#> 
#>  Term Estimate Std. Error     z Pr(>|z|)    S   2.5 %   97.5 %
#>   cyl   -0.942     0.5517 -1.71   0.0879  3.5 -2.0229  0.13968
#>   hp    -0.018     0.0119 -1.52   0.1288  3.0 -0.0413  0.00524
#>   wt    -3.167     0.7406 -4.28   <0.001 15.7 -4.6185 -1.71549
#> 
#> Type: response
#> Comparison: dY/dX

36.2.4 Counterfactual Average Marginal Effects

The at argument of the margins package emulates Stata by fixing the values of some variables at user-specified values, and by replicating the full dataset several times for each combination of the supplied values (see the Stata section below). For example, if the dataset includes 32 rows and the user calls at=list(cyl=c(4, 6)), margins will compute 64 unit-level marginal effects estimates:

dat <- mtcars
dat$cyl <- factor(dat$cyl)
mod <- lm(mpg ~ cyl * hp + wt, data = mtcars)

mar <- margins(mod, at = list(cyl = c(4, 6, 8)))
summary(mar)
#>  factor    cyl     AME     SE       z      p   lower   upper
#>     cyl 4.0000  0.0381 0.5999  0.0636 0.9493 -1.1376  1.2139
#>     cyl 6.0000  0.0381 0.5999  0.0636 0.9493 -1.1376  1.2139
#>     cyl 8.0000  0.0381 0.5999  0.0636 0.9493 -1.1376  1.2139
#>      hp 4.0000 -0.0878 0.0267 -3.2937 0.0010 -0.1400 -0.0355
#>      hp 6.0000 -0.0499 0.0154 -3.2397 0.0012 -0.0800 -0.0197
#>      hp 8.0000 -0.0120 0.0108 -1.1065 0.2685 -0.0332  0.0092
#>      wt 4.0000 -3.1198 0.6613 -4.7175 0.0000 -4.4160 -1.8236
#>      wt 6.0000 -3.1198 0.6613 -4.7175 0.0000 -4.4160 -1.8236
#>      wt 8.0000 -3.1198 0.6613 -4.7175 0.0000 -4.4160 -1.8236

avg_slopes(
    mod,
    by = "cyl",
    newdata = datagrid(cyl = c(4, 6, 8)), grid_type = "counterfactual")
#> 
#>  Term cyl Estimate Std. Error       z Pr(>|z|)    S   2.5 %   97.5 %
#>   cyl   4   0.0441     0.6011  0.0733  0.94157  0.1 -1.1341  1.22219
#>   cyl   6   0.0441     0.6012  0.0733  0.94158  0.1 -1.1343  1.22241
#>   cyl   8   0.0441     0.6012  0.0733  0.94158  0.1 -1.1343  1.22241
#>   hp    4  -0.0878     0.0267 -3.2935  < 0.001 10.0 -0.1400 -0.03554
#>   hp    6  -0.0499     0.0154 -3.2401  0.00119  9.7 -0.0800 -0.01970
#>   hp    8  -0.0120     0.0108 -1.1065  0.26851  1.9 -0.0332  0.00923
#>   wt    4  -3.1198     0.6614 -4.7172  < 0.001 18.7 -4.4161 -1.82357
#>   wt    6  -3.1198     0.6612 -4.7182  < 0.001 18.7 -4.4158 -1.82384
#>   wt    8  -3.1198     0.6614 -4.7173  < 0.001 18.7 -4.4161 -1.82358
#> 
#> Type: response
#> Comparison: dY/dX

36.2.5 Adjusted Predictions

The syntax to compute adjusted predictions using the predictions package or marginaleffects is very similar:

prediction::prediction(mod) |> head()
#>    mpg cyl disp  hp drat    wt  qsec vs am gear carb   fitted se.fitted
#> 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 21.90488 0.6927034
#> 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 21.10933 0.6266557
#> 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 25.64753 0.6652076
#> 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 20.04859 0.6041400
#> 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 17.25445 0.7436172
#> 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 19.53360 0.6436862

marginaleffects::predictions(mod) |> head()
#> 
#>  Estimate Std. Error    z Pr(>|z|)     S 2.5 % 97.5 %
#>      21.9      0.693 31.6   <0.001 726.6  20.5   23.3
#>      21.1      0.627 33.7   <0.001 823.9  19.9   22.3
#>      25.6      0.665 38.6   <0.001   Inf  24.3   27.0
#>      20.0      0.604 33.2   <0.001 799.8  18.9   21.2
#>      17.3      0.744 23.2   <0.001 393.2  15.8   18.7
#>      19.5      0.644 30.3   <0.001 669.5  18.3   20.8
#> 
#> Type: response

36.3 Stata

Stata is a good but expensive software package for statistical analysis. It is published by StataCorp LLC. This section compares Stata’s margins command to marginaleffects.

The results produced by marginaleffects are extensively tested against Stata. See the test suite for a list of the dozens of models where we compared estimates and standard errors.

36.3.1 Average Marginal Effect (AMEs)

Marginal effects are unit-level quantities. To compute “average marginal effects”, we first calculate marginal effects for each observation in a dataset. Then, we take the mean of those unit-level marginal effects.

36.3.1.1 Stata

Both Stata’s margins command and the slopes function can calculate average marginal effects (AMEs). Here is an example showing how to estimate AMEs in Stata:

quietly reg mpg cyl hp wt
margins, dydx(*)

Average marginal effects                        Number of obs     =         32
Model VCE    : OLS
 
Expression   : Linear prediction, predict()
dy/dx w.r.t. : cyl hp wt
 
------------------------------------------------------------------------------
    |            Delta-method
    |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------------------------------------------------------
cyl |  -.9416168   .5509164    -1.71   0.098    -2.070118    .1868842
 hp |  -.0180381   .0118762    -1.52   0.140    -.0423655    .0062893
 wt |  -3.166973   .7405759    -4.28   0.000    -4.683974   -1.649972
------------------------------------------------------------------------------

36.3.1.2 marginaleffects

The same results can be obtained with slopes() and summary() like this:

library("marginaleffects")
mod <- lm(mpg ~ cyl + hp + wt, data = mtcars)
avg_slopes(mod)
#> 
#>  Term Estimate Std. Error     z Pr(>|z|)    S   2.5 %   97.5 %
#>   cyl   -0.942     0.5512 -1.71   0.0876  3.5 -2.0220  0.13879
#>   hp    -0.018     0.0119 -1.52   0.1288  3.0 -0.0413  0.00524
#>   wt    -3.167     0.7406 -4.28   <0.001 15.7 -4.6184 -1.71552
#> 
#> Type: response
#> Comparison: dY/dX

Note that Stata reports t statistics while marginaleffects reports Z. This produces slightly different p-values because this model has low degrees of freedom: mtcars only has 32 rows

36.3.2 Counterfactual Marginal Effects

A “counterfactual marginal effect” is a special quantity obtained by replicating a dataset while fixing some regressor to user-defined values.

Concretely, Stata computes counterfactual marginal effects in 3 steps:

1. Duplicate the whole dataset 3 times and sets the values of cyl to the three specified values in each of those subsets.
2. Calculate marginal effects for each observation in that large grid.
3. Take the average of marginal effects for each value of the variable of interest.

36.3.2.1 Stata

With the at argument, Stata’s margins command estimates average counterfactual marginal effects. Here is an example:

quietly reg mpg i.cyl##c.hp wt
margins, dydx(hp) at(cyl = (4 6 8))

Average marginal effects                        Number of obs     =         32
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : hp

1._at        : cyl             =           4

2._at        : cyl             =           6

3._at        : cyl             =           8

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hp           |
         _at |
          1  |   -.099466   .0348665    -2.85   0.009    -.1712749   -.0276571
          2  |  -.0213768    .038822    -0.55   0.587    -.1013323    .0585787
          3  |   -.013441   .0125138    -1.07   0.293    -.0392137    .0123317
------------------------------------------------------------------------------

36.3.2.2 marginaleffects

You can estimate average counterfactual marginal effects with slopes() by using the datagrid() to create a counterfactual dataset in which the full original dataset is replicated for each potential value of the cyl variable. Then, we tell the by argument to average within groups:

mod <- lm(mpg ~ as.factor(cyl) * hp + wt, data = mtcars)

avg_slopes(
    mod,
    variables = "hp",
    by = "cyl",
    newdata = datagrid(cyl = c(4, 6, 8), grid_type = "counterfactual"))
#> 
#>  cyl Estimate Std. Error      z Pr(>|z|)   S   2.5 %  97.5 %
#>    4  -0.0995     0.0349 -2.853  0.00433 7.9 -0.1678 -0.0311
#>    6  -0.0214     0.0388 -0.551  0.58189 0.8 -0.0975  0.0547
#>    8  -0.0134     0.0125 -1.074  0.28278 1.8 -0.0380  0.0111
#> 
#> Term: hp
#> Type: response
#> Comparison: dY/dX

This is equivalent to taking the group-wise mean of observation-level marginal effects (without the by argument):

mfx <- slopes(
    mod,
    variables = "hp",
    newdata = datagrid(cyl = c(4, 6, 8), grid_type = "counterfactual"))
aggregate(estimate ~ term + cyl, data = mfx, FUN = mean)
#>   term cyl    estimate
#> 1   hp   4 -0.09946598
#> 2   hp   6 -0.02137679
#> 3   hp   8 -0.01344103

Note that following Stata, the standard errors for group-averaged marginal effects are computed by taking the “Jacobian at the mean:”

J <- components(mfx, "jacobian")
J_mean <- aggregate(J, by = list(mfx$cyl), FUN = mean)
J_mean <- as.matrix(J_mean[, 2:ncol(J_mean)])
sqrt(diag(J_mean %*% vcov(mod) %*% t(J_mean)))
#> [1] 0.03486634 0.03882234 0.01251371

36.3.3 Average Counterfactual Adjusted Predictions

36.3.3.1 Stata

Just like Stata’s margins command computes average counterfactual marginal effects, it can also estimate average counterfactual adjusted predictions.

Here is an example:

quietly reg mpg i.cyl##c.hp wt
margins, at(cyl = (4 6 8))

Predictive margins                              Number of obs     =         32
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : cyl             =           4

2._at        : cyl             =           6

3._at        : cyl             =           8

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   17.44233   2.372914     7.35   0.000     12.55522    22.32944
          2  |    18.9149   1.291483    14.65   0.000     16.25505    21.57476
          3  |   18.33318   1.123874    16.31   0.000     16.01852    20.64785
------------------------------------------------------------------------------

Again, this is what Stata does in the background:

1. It duplicates the whole dataset 3 times and sets the values of cyl to the three specified values in each of those subsets.
2. It calculates predictions for that large grid.
3. It takes the average prediction for each value of cyl.

In other words, average counterfactual adjusted predictions as implemented by Stata are a hybrid between predictions at the observed values (the default in marginaleffects::predictions) and predictions at representative values.

36.3.3.2 marginaleffects

You can estimate average counterfactual adjusted predictions with predictions() by, first, setting the grid_type argument of datagrid() to "counterfactual" and, second, by averaging the predictions using the by argument of summary(), or a manual function like dplyr::summarise().

mod <- lm(mpg ~ as.factor(cyl) * hp + wt, data = mtcars)

predictions(
    mod,
    by = "cyl",
    newdata = datagrid(cyl = c(4, 6, 8), grid_type = "counterfactual"))
#> 
#>  cyl Estimate Std. Error     z Pr(>|z|)     S 2.5 % 97.5 %
#>    4     17.4       2.37  7.35   <0.001  42.2  12.8   22.1
#>    6     18.9       1.29 14.65   <0.001 158.9  16.4   21.4
#>    8     18.3       1.12 16.31   <0.001 196.3  16.1   20.5
#> 
#> Type: response

predictions(
    mod,
    newdata = datagrid(cyl = c(4, 6, 8), grid_type = "counterfactual")) |>
    group_by(cyl) |>
    summarize(AAP = mean(estimate))
#> # A tibble: 3 × 2
#>   cyl     AAP
#>   <fct> <dbl>
#> 1 4      17.4
#> 2 6      18.9
#> 3 8      18.3

36.4 brmsmargins

The brmsmargins package is developed by Joshua Wiley:

This package has functions to calculate marginal effects from brms models ( http://paul-buerkner.github.io/brms/ ). A central motivator is to calculate average marginal effects (AMEs) for continuous and discrete predictors in fixed effects only and mixed effects regression models including location scale models.

The main advantage of brmsmargins over marginaleffects is its ability to compute “Marginal Coefficients” following the method described in Hedeker et al (2012).

The main advantages of marginaleffects over brmsmargins are:

1. Support for 60+ model types, rather than just the brms package.
2. Simpler user interface (subjective).
3. At the time of writing (2022-05-25) brmsmargins did not support certain brms models such as those with multivariate or multinomial outcomes. It also did not support custom outcome transformations.

The rest of this section presents side-by-side replications of some of the analyses from the brmsmargins vignettes in order to show highlight parallels and differences in syntax.

36.4.1 Marginal Effects for Fixed Effects Models

36.4.1.1 AMEs for Logistic Regression

Estimate a logistic regression model with brms:

library(brms)
library(brmsmargins)
library(marginaleffects)
library(data.table)
library(withr)
setDTthreads(5)
h <- 1e-4

void <- capture.output(
    bayes.logistic <- brm(
      vs ~ am + mpg, data = mtcars,
      family = "bernoulli", seed = 1234,
      silent = 2, refresh = 0,
      backend = "cmdstanr",
      chains = 4L, cores = 4L)
)

Compute AMEs manually:

d1 <- d2 <- mtcars
d2$mpg <- d2$mpg + h
p1 <- posterior_epred(bayes.logistic, newdata = d1)
p2 <- posterior_epred(bayes.logistic, newdata = d2)
m <- (p2 - p1) / h
quantile(rowMeans(m), c(.5, .025, .975))
#>        50%       2.5%      97.5% 
#> 0.06981152 0.05372040 0.09159040

Compute AMEs with brmsmargins:

bm <- brmsmargins(
  bayes.logistic,
  add = data.frame(mpg = c(0, 0 + h)),
  contrasts = cbind("AME MPG" = c(-1 / h, 1 / h)),
  CI = 0.95,
  CIType = "ETI")
data.frame(bm$ContrastSummary)
#>            M        Mdn        LL        UL PercentROPE PercentMID   CI CIType ROPE  MID   Label
#> 1 0.07098514 0.06981152 0.0537204 0.0915904          NA         NA 0.95    ETI <NA> <NA> AME MPG

Compute AMEs using marginaleffects:

avg_slopes(bayes.logistic) 
#> 
#>  Term Contrast Estimate   2.5 %  97.5 %
#>   am     1 - 0  -0.2672 -0.4219 -0.0682
#>   mpg    dY/dX   0.0698  0.0537  0.0916
#> 
#> Type: response

The mpg element of the Effect column from marginaleffects matches the the M column of the output from brmsmargins.

36.4.2 Marginal Effects for Mixed Effects Models

Estimate a mixed effects logistic regression model with brms:

d <- withr::with_seed(
  seed = 12345, code = {
    nGroups <- 100
    nObs <- 20
    theta.location <- matrix(rnorm(nGroups * 2), nrow = nGroups, ncol = 2)
    theta.location[, 1] <- theta.location[, 1] - mean(theta.location[, 1])
    theta.location[, 2] <- theta.location[, 2] - mean(theta.location[, 2])
    theta.location[, 1] <- theta.location[, 1] / sd(theta.location[, 1])
    theta.location[, 2] <- theta.location[, 2] / sd(theta.location[, 2])
    theta.location <- theta.location %*% chol(matrix(c(1.5, -.25, -.25, .5^2), 2))
    theta.location[, 1] <- theta.location[, 1] - 2.5
    theta.location[, 2] <- theta.location[, 2] + 1
    d <- data.table(
      x = rep(rep(0:1, each = nObs / 2), times = nGroups))
    d[, ID := rep(seq_len(nGroups), each = nObs)]

    for (i in seq_len(nGroups)) {
      d[ID == i, y := rbinom(
        n = nObs,
        size = 1,
        prob = plogis(theta.location[i, 1] + theta.location[i, 2] * x))
        ]
    }
    copy(d)
  })

void <- capture.output(
    mlogit <- brms::brm(
      y ~ 1 + x + (1 + x | ID), family = "bernoulli",
      data = d, seed = 1234,
      backend = "cmdstanr",
      silent = 2, refresh = 0,
      chains = 4L, cores = 4L)
)

36.4.2.1 AME: Including Random Effects

bm <- brmsmargins(
  mlogit,
  add = data.frame(x = c(0, h)),
  contrasts = cbind("AME x" = c(-1 / h, 1 / h)),
  effects = "includeRE",
  CI = .95,
  CIType = "ETI")
data.frame(bm$ContrastSummary)
#>           M       Mdn        LL        UL PercentROPE PercentMID   CI CIType ROPE  MID Label
#> 1 0.1118135 0.1120303 0.0814249 0.1425242          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(mlogit)
#> 
#>  Estimate 2.5 % 97.5 %
#>     0.111 0.081  0.141
#> 
#> Term: x
#> Type: response
#> Comparison: 1 - 0

36.4.2.2 AME: Fixed Effects Only (Grand Mean)

bm <- brmsmargins(
  mlogit,
  add = data.frame(x = c(0, h)),
  contrasts = cbind("AME x" = c(-1 / h, 1 / h)),
  effects = "fixedonly",
  CI = .95,
  CIType = "ETI")
data.frame(bm$ContrastSummary)
#>           M       Mdn         LL        UL PercentROPE PercentMID   CI CIType ROPE  MID Label
#> 1 0.1040304 0.1037629 0.06163523 0.1480365          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(mlogit, re_formula = NA)
#> 
#>  Estimate  2.5 % 97.5 %
#>     0.101 0.0607  0.142
#> 
#> Term: x
#> Type: response
#> Comparison: 1 - 0

36.4.3 Marginal Effects for Location Scale Models

36.4.3.1 AMEs for Fixed Effects Location Scale Models

Estimate a fixed effects location scale model with brms:

d <- withr::with_seed(
  seed = 12345, code = {
    nObs <- 1000L
    d <- data.table(
      grp = rep(0:1, each = nObs / 2L),
      x = rnorm(nObs, mean = 0, sd = 0.25))
    d[, y := rnorm(nObs,
                   mean = x + grp,
                   sd = exp(1 + x + grp))]
    copy(d)
  })

void <- capture.output(
    ls.fe <- brm(bf(
      y ~ 1 + x + grp,
      sigma ~ 1 + x + grp),
      family = "gaussian",
      data = d, seed = 1234,
      silent = 2, refresh = 0,
      backend = "cmdstanr",
      chains = 4L, cores = 4L)
)

36.4.3.2 Fixed effects only

bm <- brmsmargins(
  ls.fe,
  add = data.frame(x = c(0, h)),
  contrasts = cbind("AME x" = c(-1 / h, 1 / h)),
  CI = 0.95, CIType = "ETI",
  effects = "fixedonly")
data.frame(bm$ContrastSummary)
#>         M     Mdn        LL       UL PercentROPE PercentMID   CI CIType ROPE  MID Label
#> 1 1.63427 1.62503 0.7653377 2.505649          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(ls.fe, re_formula = NA)
#> 
#>  Term Contrast Estimate 2.5 % 97.5 %
#>   grp    1 - 0     1.02 0.371   1.69
#>   x      dY/dX     1.63 0.765   2.51
#> 
#> Type: response

36.4.3.3 Discrete change and distributional parameter (dpar)

Compute the contrast between adjusted predictions on the sigma parameter, when grp=0 and grp=1:

bm <- brmsmargins(
  ls.fe,
  at = data.frame(grp = c(0, 1)),
  contrasts = cbind("AME grp" = c(-1, 1)),
  CI = 0.95, CIType = "ETI", dpar = "sigma",
  effects = "fixedonly")
data.frame(bm$ContrastSummary)
#>          M      Mdn       LL       UL PercentROPE PercentMID   CI CIType ROPE  MID   Label
#> 1 4.908447 4.906174 4.420461 5.428851          NA         NA 0.95    ETI <NA> <NA> AME grp

In marginaleffects we use the comparisons() function and the variables argument:

avg_comparisons(
  ls.fe,
  variables = list(grp = 0:1),
  dpar = "sigma")
#> 
#>  Estimate 2.5 % 97.5 %
#>      4.91  4.42   5.43
#> 
#> Term: grp
#> Type: response
#> Comparison: 1 - 0

36.4.3.4 Marginal effect (continuous) on sigma

bm <- brmsmargins(
  ls.fe,
  add = data.frame(x = c(0, h)),
  contrasts = cbind("AME x" = c(-1 / h, 1 / h)),
  CI = 0.95, CIType = "ETI", dpar = "sigma",
  effects = "fixedonly")
data.frame(bm$ContrastSummary)
#>          M      Mdn       LL       UL PercentROPE PercentMID   CI CIType ROPE  MID Label
#> 1 4.461043 4.457211 3.532813 5.434782          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(ls.fe, dpar = "sigma", re_formula = NA)
#> 
#>  Term Contrast Estimate 2.5 % 97.5 %
#>   grp    1 - 0     4.91  4.42   5.43
#>   x      dY/dX     4.46  3.53   5.43
#> 
#> Type: response

36.5 fmeffects

The fmeffects package is described as follows:

fmeffects: Model-Agnostic Interpretations with Forward Marginal Effects. Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and ‘fmeffects’ computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022)

As the name says, this package is focused on “forward marginal effects” in the context of machine learning models estimated using the mlr3 or tidymodels frameworks. Since version 0.16.0, marginaleffects also supports these machine learning frameworks, and it covers a superset of the fmeffects functionality. Consider a random forest model trained on the bikes data:

library("mlr3verse")
library("fmeffects")
data("bikes", package = "fmeffects")
task <- as_task_regr(x = bikes, id = "bikes", target = "count")
forest <- lrn("regr.ranger")$train(task)

Now, we use the avg_comparisons() function to compute forward marginal effects:

avg_comparisons(forest, variables = list(temp = 1), newdata = bikes)

This is equivalent to the key quantity reported by the fmeffects package:

mfx <- fme(model = forest, data = bikes, features = list("temp" = 1))
print(mfx)

Another interesting feature of fmeffects is the ability treat categorical predictors in an unconventional way: pick a reference level, then compute the average difference between the predicted values for that level, and the predicted values for the observed levels (which may be the same as the reference level).

In the bikes example, we can answer the question: how does the expected number of bike rentals increases, on average, if all days were misty? With marginaleffects, we can use a function in the variables argument to specify a custom contrast:

FUN <- function(x) data.frame(lo = x, hi = "misty")

avg_comparisons(
  forest,
  newdata = bikes,
  variables = list(weather = FUN)
)

Two more functionalities deserve to be highlight. First, fmeffects includes functions to explore heterogeneity in marginal effects using recursive partitioning trees. The heterogeneity vignette illustrates how to achieve something similar with marginaleffects.

Second, fmeffects also implements a non-linearity measure. At the moment, there is no analogue to this in marginaleffects.

36.6 effects

The effects package was created by John Fox and colleagues.

• marginaleffects supports 30+ more model types than effects.
• effects focuses on the computation of “adjusted predictions.” The plots it produces are roughly equivalent to the ones produced by the plot_predictions and predictions functions in marginaleffects.
• effects does not appear support marginal effects (slopes), marginal means, or contrasts
• effects uses Base graphics whereas marginaleffects uses ggplot2
• effects includes a lot of very powerful options to customize plots. In contrast, marginaleffects produces objects which can be customized by chaining ggplot2 functions. Users can also call plot_predictions(model, draw=FALSE) to create a prediction grid, and then work the raw data directly to create the plot they need

effects offers several options which are not currently available in marginaleffects, including:

• Partial residuals plots
• Many types of ways to plot adjusted predictions: package vignette

36.7 modelbased

The modelbased package is developed by the easystats team.

This section is incomplete; contributions are welcome.

• Wrapper around emmeans to compute marginal means and marginal effects.
• Powerful functions to create beautiful plots.

36.8 ggeffects

The ggeffects package is developed by Daniel Lüdecke.

This section is incomplete; contributions are welcome.

• Wrapper around emmeans to compute marginal means.