Using ggeffects with nestedLogit models

Michael Friendly

2026-02-24

Load the packages we’ll use here:

library(nestedLogit)    # Nested Dichotomy Logistic Regression Models
library(ggeffects)      # Create Tidy Data Frames of Marginal Effects
library(ggplot2)        # Data Visualisations Using the Grammar of Graphics

Overview

The ggeffects package (Lüdecke, 2018, 2025) provides a simple and unified interface for computing and plotting adjusted predictions and marginal effects from a wide variety of regression models. Its main function, predict_response(), returns a tidy data frame of model predictions that can be plotted directly with a built-in plot() method or further customized with ggplot2.

The package now supports "nestedLogit" objects, making it easy to visualize predicted probabilities for each response category across levels of the predictors, without the manual data wrangling described in vignette("plotting-ggplot", package = "nestedLogit").

Women’s labor force participation

We use the standard Womenlf example from the main vignette. The response partic has three categories — not working, working part-time, and working full-time — modeled as nested dichotomies against husband’s income and presence of young children.

data(Womenlf, package = "carData")
comparisons <- logits(work = dichotomy("not.work", c("parttime", "fulltime")),
                      full = dichotomy("parttime", "fulltime"))

wlf.nested <- nestedLogit(partic ~ hincome + children,
                          dichotomies = comparisons,
                          data = Womenlf)

Predicted probabilities with predict_response()

The simplest way to obtain predicted probabilities and a plot is with predict_response(), specifying the focal predictors in the terms argument. This returns predicted probabilities for each response level, with confidence intervals, averaged over the non-focal predictors.

wlf.pred <- predict_response(wlf.nested, terms = c("hincome", "children"))
wlf.pred
#> # Predicted probabilities of partic
#> 
#> partic: not.work
#> children: absent
#> 
#> hincome | Predicted |     95% CI
#> --------------------------------
#>       0 |      0.21 | 0.11, 0.36
#>      12 |      0.30 | 0.21, 0.42
#>      22 |      0.40 | 0.28, 0.54
#>      46 |      0.65 | 0.34, 0.87
#> 
#> partic: not.work
#> children: present
#> 
#> hincome | Predicted |     95% CI
#> --------------------------------
#>       0 |      0.56 | 0.40, 0.70
#>      12 |      0.68 | 0.60, 0.75
#>      22 |      0.76 | 0.67, 0.83
#>      46 |      0.90 | 0.71, 0.97
#> 
#> partic: parttime
#> children: absent
#> 
#> hincome | Predicted |     95% CI
#> --------------------------------
#>       0 |      0.02 | 0.01, 0.10
#>      12 |      0.07 | 0.03, 0.15
#>      22 |      0.15 | 0.07, 0.29
#>      46 |      0.29 | 0.10, 0.60
#> 
#> partic: parttime
#> children: present
#> 
#> hincome | Predicted |     95% CI
#> --------------------------------
#>       0 |      0.13 | 0.06, 0.29
#>      12 |      0.20 | 0.14, 0.27
#>      22 |      0.19 | 0.13, 0.28
#>      46 |      0.10 | 0.03, 0.28
#> 
#> partic: fulltime
#> children: absent
#> 
#> hincome | Predicted |     95% CI
#> --------------------------------
#>       0 |      0.77 | 0.62, 0.87
#>      12 |      0.63 | 0.51, 0.73
#>      22 |      0.45 | 0.32, 0.60
#>      46 |      0.07 | 0.01, 0.41
#> 
#> partic: fulltime
#> children: present
#> 
#> hincome | Predicted |     95% CI
#> --------------------------------
#>       0 |      0.31 | 0.18, 0.47
#>      12 |      0.12 | 0.08, 0.19
#>      22 |      0.04 | 0.02, 0.10
#>      46 |      0.00 | 0.00, 0.03

The default plot() method produces a panel for each response category:

plot(wlf.pred)

Predicted probabilities from predict_response() with default plot.

Customizing the plot

The plot() method returns a ggplot object, so it can be further customized with standard ggplot2 functions. For example, we can adjust the line size, labels, theme, and legend position:

plot(wlf.pred,
     line_size = 2) +
  labs(title = "Predicted Probabilities of Work by Husband's Income",
       y = "Probability",
       x = "Husband's Income") +
  theme_ggeffects(base_size = 16) +
  theme(legend.position = "top")

Customized ggeffects plot with adjusted labels and theme.

Plotting on the logit scale

ggplot2 provides a built-in "logit" transformation for axes via scale_y_continuous(transform = "logit"). This displays predicted probabilities on the logit scale, $\text{logit}(p) = \log(p / (1-p))$, where the axis labels remain as probabilities but their spacing reflects the logit transformation. This is useful because the logistic regression model is linear on the logit scale, so the predicted curves appear as straighter lines.

Since the plot() method for ggeffects returns a ggplot object, we can simply add this scale transformation. Note that we need to specify breaks manually, because the automatic break algorithm does not work well with the logit transformation.

plot(wlf.pred,
     line_size = 2) +
  scale_y_continuous(
    transform = "logit",
    breaks = c(0.05, 0.10, 0.25, 0.50, 0.75, 0.90, 0.95)
  ) +
  labs(title = "Predicted Probabilities (logit scale)",
       y = "Probability (logit scale)",
       x = "Husband's Income") +
  theme_ggeffects(base_size = 16) +
  theme(legend.position = "top")

Predicted probabilities on the logit scale.

Alligator food choice

As a simpler example with a single continuous predictor, we fit a nested logit model to the gators data, predicting primary food choice from alligator length. The first dichotomy contrasts {Other} vs. {Fish, Invertebrates}, and the second contrasts {Fish} vs. {Invertebrates}.

data(gators)
gators.nested <- nestedLogit(food ~ length,
                             dichotomies = logits(
                               other   = dichotomy("Other", c("Fish", "Invertebrates")),
                               fish_inv = dichotomy("Fish", "Invertebrates")),
                             data = gators)

predict_response(gators.nested, terms = "length") |> 
  plot(line_size = 2)

Predicted food choice probabilities for alligators by length.

As you can see the main thing going on here is that larger alligators prefer fish, which smaller ones prefer invertebrates.

For comparison, the basic nestedLogit::plot() method using default arguments gives a similar plot, with the three curves overlaid in a single panel (it uses graphics::matplot()).

plot(gators.nested, x.var = "length", 
     legend.bty = "o")

Predicted food choice probabilities for alligators by length.

Limitations

The ggeffects package computes and plots predicted probabilities for the response categories of a nested logit model. As shown above, these can be displayed on the logit scale using ggplot2’s built-in axis transformation.

However, ggeffects does not currently provide access to the individual dichotomy sub-models that comprise the nested logit — for example, plotting predicted values for the work and full dichotomies separately.

For these more specialized displays, see vignette("plotting-ggplot", package = "nestedLogit"), which describes a manual workflow using predict() with model = "dichotomies" and as.data.frame() to construct fully customized ggplot2 plots.

References

Lüdecke, D. (2018). Ggeffects: Tidy data frames of marginal effects from regression models. Journal of Open Source Software, 3(26), 772. http://doi.org/10.21105/joss.00772

Lüdecke, D. (2025). Ggeffects: Create tidy data frames of marginal effects for ggplot from model outputs. Retrieved from https://strengejacke.github.io/ggeffects/