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

## 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 marginal_means function which can also compute 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 marginal effects and unit-level marginal effects for whole datasets.
• Easier to compute marginal effects (slopes) for custom grids and continuous regressors.
• Easier to implement causal inference strategies like the parametric g-formula and regression adjustement 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.

AFAICT, the main advantages of emmeans over marginaleffects are:

• Omnibus tests.
• Equivalence and noninferiority tests.

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.

### 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)

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 4367.17 Inf  -0.004  0.9964
#>  cyl8 - cyl6  -18.637 4367.16 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,
newdata = "mean",
variables = list(cyl = "pairwise"))
#>
#>  Term Contrast Estimate Std. Error         z Pr(>|z|)   2.5 %  97.5 %
#>   cyl    6 - 4  -0.9049       1.63 -0.555059  0.57885    -4.1    2.29
#>   cyl    8 - 4 -19.5418    4367.17 -0.004475  0.99643 -8579.0 8539.95
#>   cyl    8 - 6 -18.6369    4367.17 -0.004268  0.99660 -8578.1 8540.85
#>
#> Columns: rowid, type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, vs, hp, cyl

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|)      2.5 %    97.5 %
#>    hp       +1 -1.558e-10  6.804e-07 -0.000229  0.99982 -1.334e-06 1.333e-06
#>   cyl    6 - 4 -2.222e-01  3.916e-01 -0.567315  0.57050 -9.898e-01 5.454e-01
#>   cyl    8 - 4 -5.947e-01  5.097e-01 -1.166640  0.24336 -1.594e+00 4.044e-01
#>
#> Prediction type:  response
#> Columns: rowid, type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, vs, hp, cyl, eps

### 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$woman <- as.numeric(dat$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 0.1097 Inf  -0.139  0.8898
#>  shout - curse  -0.2533 0.1022 Inf  -2.479  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 0.1178 Inf  -2.031  0.0422
#>  shout - curse  -0.0834 0.1330 Inf  -0.627  0.5309
#>  shout - scold   0.1559 0.1358 Inf   1.148  0.2510
#>
#> resp = yes:
#>  contrast      estimate     SE  df z.ratio p.value
#>  scold - curse   0.0391 0.1292 Inf   0.302  0.7624
#>  shout - curse   0.5802 0.1784 Inf   3.252  0.0011
#>  shout - scold   0.5411 0.1888 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") #> #> Term Contrast resp Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % #> btype mean(scold) - mean(curse) no -0.01520 0.10965 -0.1386 0.8897581 -0.2301 0.199715 #> btype mean(scold) - mean(curse) perhaps -0.23928 0.11779 -2.0314 0.0422130 -0.4701 -0.008416 #> btype mean(scold) - mean(curse) yes 0.03907 0.12922 0.3023 0.7623876 -0.2142 0.292344 #> btype mean(shout) - mean(curse) no -0.25330 0.10219 -2.4786 0.0131889 -0.4536 -0.053004 #> btype mean(shout) - mean(curse) perhaps -0.08336 0.13303 -0.6266 0.5309040 -0.3441 0.177372 #> btype mean(shout) - mean(curse) yes 0.58018 0.17842 3.2518 0.0011468 0.2305 0.929873 #> btype mean(shout) - mean(scold) no -0.23810 0.08864 -2.6860 0.0072316 -0.4118 -0.064358 #> btype mean(shout) - mean(scold) perhaps 0.15592 0.13583 1.1479 0.2510250 -0.1103 0.422149 #> btype mean(shout) - mean(scold) yes 0.54111 0.18881 2.8660 0.0041574 0.1711 0.911161 #> #> Prediction type: link #> Columns: type, term, contrast, resp, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo These results are identical to those produced by emmeans (except for $$t$$ vs. $$z$$). ### 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 Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % cyl #> hp dY/dX -0.007852 0.01111 -0.7069 0.47964 -0.02962 0.01392 4 #> cyl 6 - 4 -0.222185 0.39164 -0.5673 0.57050 -0.98979 0.54542 4 #> cyl 8 - 4 -0.594685 0.50974 -1.1666 0.24336 -1.59376 0.40439 4 #> #> Prediction type: response #> Columns: rowid, type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, vs, hp, cyl, eps 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 Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % cyl #> hp dY/dX -0.03257 3.388e-02 -0.961445 0.33633 -9.898e-02 3.383e-02 4 #> cyl 6 - 4 -0.90487 1.630e+00 -0.555059 0.57885 -4.100e+00 2.290e+00 4 #> cyl 8 - 4 -19.54176 4.367e+03 -0.004475 0.99643 -8.579e+03 8.540e+03 4 #> #> Prediction type: link #> Columns: rowid, type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, vs, hp, cyl, eps ### 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") marginal_means(m, variables = "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, effect = "am", condition = "wt") # You of course customize everything, set draw=FALSE, and feed the raw data to feed to ggplot2 p <- plot_comparisons( m, effect = "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)) ## 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. ### 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) ### 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 0.3035105 0.0001410451 0.5484521 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 0.3035105 0.0001410451 0.5484521 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 0.3035105 0.0001410451 0.5484521 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 0.3035105 0.0001410451 0.5484521 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 0.3035105 0.0001410451 0.5484521 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 0.3035105 0.0001410451 0.5484521 NA 1 head(mfx) #> #> Term Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % #> cyl -0.9416 0.5509 -1.709 0.087417 -2.021 0.1382 #> cyl -0.9416 0.5509 -1.709 0.087417 -2.021 0.1382 #> cyl -0.9416 0.5509 -1.709 0.087417 -2.021 0.1382 #> cyl -0.9416 0.5509 -1.709 0.087417 -2.021 0.1382 #> cyl -0.9416 0.5509 -1.709 0.087417 -2.021 0.1382 #> cyl -0.9416 0.5509 -1.709 0.087417 -2.021 0.1382 #> #> Prediction type: response #> Columns: rowid, type, term, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, eps nd <- data.frame(cyl = 4, hp = 110, wt = 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 0.0001410457 0.54846 0.5509157 0.01187627 0.7405808 NA 1 slopes(mod, newdata = "mean") #> #> Term Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % #> cyl -0.94162 0.55092 -1.709 0.087417 -2.02139 0.138160 #> hp -0.01804 0.01188 -1.519 0.128803 -0.04132 0.005239 #> wt -3.16697 0.74058 -4.276 1.8997e-05 -4.61847 -1.715471 #> #> Prediction type: response #> Columns: rowid, type, term, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, mpg, cyl, hp, wt, eps ### 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 0.6000 0.0636 0.9493 -1.1378 1.2141 #> 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 = datagridcf(cyl = c(4, 6, 8))) #> #> Term Contrast Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % cyl #> cyl mean(dY/dX) 0.03814 0.59989 0.06357 0.94930949 -1.13762 1.213899 4 #> cyl mean(dY/dX) 0.03814 0.59989 0.06357 0.94930949 -1.13762 1.213899 6 #> cyl mean(dY/dX) 0.03814 0.59989 0.06357 0.94930949 -1.13762 1.213899 8 #> hp mean(dY/dX) -0.08778 0.02665 -3.29366 0.00098892 -0.14002 -0.035545 4 #> hp mean(dY/dX) -0.04987 0.01539 -3.23974 0.00119639 -0.08004 -0.019701 6 #> hp mean(dY/dX) -0.01197 0.01081 -1.10649 0.26851517 -0.03316 0.009229 8 #> wt mean(dY/dX) -3.11981 0.66132 -4.71754 2.3871e-06 -4.41598 -1.823648 4 #> wt mean(dY/dX) -3.11981 0.66132 -4.71754 2.3871e-06 -4.41598 -1.823648 6 #> wt mean(dY/dX) -3.11981 0.66132 -4.71754 2.3871e-06 -4.41598 -1.823648 8 #> #> Prediction type: response #> Columns: type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, cyl, predicted, predicted_hi, predicted_lo ### 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|) 2.5 % 97.5 % #> 21.90 0.6927 31.62 < 2.22e-16 20.55 23.26 #> 21.11 0.6267 33.69 < 2.22e-16 19.88 22.34 #> 25.65 0.6652 38.56 < 2.22e-16 24.34 26.95 #> 20.05 0.6041 33.19 < 2.22e-16 18.86 21.23 #> 17.25 0.7436 23.20 < 2.22e-16 15.80 18.71 #> 19.53 0.6437 30.35 < 2.22e-16 18.27 20.80 #> #> Prediction type: response #> Columns: rowid, type, estimate, std.error, statistic, p.value, conf.low, conf.high, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb ## 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. ### 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. #### 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 ------------------------------------------------------------------------------ #### 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|) 2.5 % 97.5 % #> cyl -0.94162 0.55092 -1.709 0.087417 -2.02139 0.138159 #> hp -0.01804 0.01188 -1.519 0.128803 -0.04132 0.005239 #> wt -3.16697 0.74058 -4.276 1.8997e-05 -4.61848 -1.715471 #> #> Prediction type: response #> Columns: type, term, estimate, std.error, statistic, p.value, conf.low, conf.high 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 ### 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. #### 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 ------------------------------------------------------------------------------ #### marginaleffects You can estimate average counterfactual marginal effects with slopes() by using the datagridcf() 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 = datagridcf(cyl = c(4, 6, 8))) #> #> Term Contrast Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % cyl #> hp mean(dY/dX) -0.09947 0.03487 -2.8528 0.004334 -0.16780 -0.03113 4 #> hp mean(dY/dX) -0.02138 0.03882 -0.5506 0.581884 -0.09747 0.05471 6 #> hp mean(dY/dX) -0.01344 0.01251 -1.0741 0.282780 -0.03797 0.01109 8 #> #> Prediction type: response #> Columns: type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, cyl, predicted, predicted_hi, predicted_lo This is equivalent to taking the group-wise mean of observation-level marginal effects (without the by argument): mfx <- slopes( mod, variables = "hp", newdata = datagridcf(cyl = c(4, 6, 8))) 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 <- attr(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] 0.03486650 0.03882204 0.01251382

#### 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.

#### 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 = datagridcf(cyl = c(4, 6, 8)))
#>
#>  cyl Estimate Std. Error      z   Pr(>|z|) 2.5 % 97.5 %
#>    4    17.44      2.373  7.351 1.9733e-13 12.79  22.09
#>    6    18.91      1.291 14.646 < 2.22e-16 16.38  21.45
#>    8    18.33      1.124 16.312 < 2.22e-16 16.13  20.54
#>
#> Prediction type:  response
#> Columns: type, cyl, estimate, std.error, statistic, p.value, conf.low, conf.high

predictions(
mod,
newdata = datagridcf(cyl = c(4, 6, 8))) |>
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

## 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.

### Marginal Effects for Fixed Effects Models

#### AMEs for Logistic Regression

Estimate a logistic regression model with brms:

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

void <- capture.output(
bayes.logistic <- brm(
vs ~ am + mpg, data = mtcars,
family = "bernoulli", seed = 1234,
silent = 2, refresh = 0,
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%
#> 0.07014014 0.05437810 0.09159280

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 0.07118446 0.07014014 0.0543781 0.0915928 NA NA 0.95 ETI <NA> <NA> AME MPG Compute AMEs using marginaleffects: avg_slopes(bayes.logistic) #> #> Term Estimate 2.5 % 97.5 % #> am -0.31810 -0.52182 -0.07808 #> mpg 0.07015 0.05439 0.09158 #> #> Prediction type: response #> Columns: type, term, estimate, conf.low, conf.high The mpg element of the Effect column from marginaleffects matches the the M column of the output from brmsmargins. ### 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, silent = 2, refresh = 0, chains = 4L, cores = 4L) ) #> Warning: There were 61 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#bulk-ess #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess #### 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 0.1113512 0.1114643 0.08037199 0.1419889          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(mlogit)
#>
#>  Term Estimate   2.5 % 97.5 %
#>     x   0.1115 0.08037  0.142
#>
#> Prediction type:  response
#> Columns: type, term, estimate, conf.low, conf.high

#### 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$ContrastSummary) #> M Mdn LL UL PercentROPE PercentMID CI CIType ROPE MID Label #> 1 0.1038071 0.1032705 0.06328158 0.1485747 NA NA 0.95 ETI <NA> <NA> AME x avg_slopes(mlogit, re_formula = NA) #> #> Term Estimate 2.5 % 97.5 % #> x 0.1033 0.06328 0.1486 #> #> Prediction type: response #> Columns: type, term, estimate, conf.low, conf.high ### Marginal Effects for Location Scale Models #### AMEs for Fixed Effects Location Scale Models Estimate a fixed effects location scale model with brms: d <- withr::with_seed( seed = 12345, code = { nObs <- 1000L d <- data.table( grp = rep(0:1, each = nObs / 2L), x = rnorm(nObs, mean = 0, sd = 0.25)) d[, y := rnorm(nObs, mean = x + grp, sd = exp(1 + x + grp))] copy(d) }) void <- capture.output( ls.fe <- brm(bf( y ~ 1 + x + grp, sigma ~ 1 + x + grp), family = "gaussian", data = d, seed = 1234, silent = 2, refresh = 0, chains = 4L, cores = 4L) ) #### Fixed effects only bm <- brmsmargins( ls.fe, add = data.frame(x = c(0, h)), contrasts = cbind("AME x" = c(-1 / h, 1 / h)), CI = 0.95, CIType = "ETI", effects = "fixedonly") data.frame(bm$ContrastSummary)
#>          M     Mdn        LL       UL PercentROPE PercentMID   CI CIType ROPE  MID Label
#> 1 1.625042 1.63264 0.7558805 2.497346          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(ls.fe, re_formula = NA)
#>
#>  Term Estimate  2.5 % 97.5 %
#>   grp    1.012 0.3482  1.678
#>     x    1.633 0.7559  2.497
#>
#> Prediction type:  response
#> Columns: type, term, estimate, conf.low, conf.high

#### 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$ContrastSummary) #> M Mdn LL UL PercentROPE PercentMID CI CIType ROPE MID Label #> 1 4.901384 4.895236 4.404996 5.417512 NA NA 0.95 ETI <NA> <NA> AME grp In marginaleffects we use the comparisons() function and the variables argument: avg_comparisons( ls.fe, variables = list(grp = 0:1), dpar = "sigma") #> #> Term Contrast Estimate 2.5 % 97.5 % #> grp 1 - 0 4.895 4.405 5.418 #> #> Prediction type: response #> Columns: type, term, contrast, estimate, conf.low, conf.high #### Marginal effect (continuous) on sigma bm <- brmsmargins( ls.fe, add = data.frame(x = c(0, h)), contrasts = cbind("AME x" = c(-1 / h, 1 / h)), CI = 0.95, CIType = "ETI", dpar = "sigma", effects = "fixedonly") data.frame(bm$ContrastSummary)
#>          M      Mdn       LL       UL PercentROPE PercentMID   CI CIType ROPE  MID Label
#> 1 4.455297 4.443977 3.500513 5.449401          NA         NA 0.95    ETI <NA> <NA> AME x

avg_slopes(ls.fe, dpar = "sigma", re_formula = NA)
#>
#>  Term Estimate 2.5 % 97.5 %
#>   grp    5.287 4.694  5.926
#>     x    4.444 3.501  5.450
#>
#> Prediction type:  response
#> Columns: type, term, estimate, conf.low, conf.high

## 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

## 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.

## ggeffects

The ggeffects package is developed by Daniel Lüdecke.

This section is incomplete; contributions are welcome.

• Wrapper around emmeans to compute marginal means.