How to get confidence intervals for predicted probability plot using {ggeffects} based on nnet::multinom() model?

Question

I want to plot the predicted probabilities for a multinomial model in R, fitted with the nnet::multinom() function. I have numerical predictors on the log scale.

Even though {ggeffects} should be compatible with multinom(), the plot does not display confidence intervals the same way it does for linear models.

I am new to using to R and to this community, so my apologies if this question is very basic or is missing something essential. Here is a small example:

library(tidyverse)
library(nnet)
library(effects)
library(ggeffects)


df <- data.frame(response = c("1 Better", "1 Better", "1 Better", "2 Medium", "2 Medium", "2 Medium", "3 Worse", "3 Worse", "3 Worse"),
                 count = c(1000, 2000, 4000, 6000, 10000, 3000, 6000, 5000, 11000))

mod1 <- multinom(response ~ log(count), data = df)
summary(mod1)

effects::effect(mod1, term="log(count)", se=TRUE, confidence.level=.95) %>% plot() # Produces CIs.

ggeffects::ggpredict(mod1, terms = "count") %>% plot() + theme_bw() # No confidence intervals.

---

If others are looking for alternatives to {ggeffects}, I tried these approaches while looking for a solution:

Using effects::effect(): works, confidence intervals are included but the appearance isn't so customisable.

Combining {ggeffects} and {effects}: See this post on R Studio Community in which confidence intervals from the effects package were combined with ggeffects to create a plot. I got the error

Error in FUN(X[[i]], ...) : object 'L' not found

But it worked for that person.

Using {MNLpred} package and its mnl_pred_ova() : didn't work for me, I think because my predictors are in log scale. I got the following error:

Error in eval(parse(text = paste0("data$", xvari))) : attempt to apply non-function

Using mnlAveEffPlot() function from {DAMisc}: Worked but the plots aren't as customisable as I would like.

Answer 1

You can do this using ggeffects::ggemmeans().

library(tidyverse)
library(ggthemes)
library(nnet)
library(ggeffects) # package version used: v0.16.0

df <- data.frame(response = c("1 Better", "1 Better", "1 Better", "2 Medium", "2 Medium", "2 Medium", "3 Worse", "3 Worse", "3 Worse"),
                 count = c(1000, 2000, 4000, 6000, 10000, 3000, 6000, 5000, 11000))

mod1 <- multinom(response ~ log(count),
                 data = df)

ggemmeans(mod1, terms = "count") %>% plot() + ggthemes::theme_tufte()

For more information on how to use {ggeffects}, you may also want to take a look at the package documentation and especially at the differences between the ggemmeans() and ggpredict() etc. (e.g. here).

The {ggeffects} package draws on the output created by {effects} but, and I believe this is what you are looking for, makes it much easier to customize the plot using standard ggplot commands.

Answer 2

The MNLpred package cannot handle the log() inside the regression function but works when you compute the log-scale beforehand.

# Packages
library(tidyverse)
library(nnet)
library(MASS)
library(MNLpred)
library(scales)
library(ggeffects)
library(ggthemes)

df <- data.frame(response = c("1 Better", "1 Better", "1 Better", 
                              "2 Medium", "2 Medium", "2 Medium", 
                              "3 Worse", "3 Worse", "3 Worse"),
                 count = c(1000, 2000, 4000, 
                           6000, 10000, 3000, 
                           6000, 5000, 11000))

mod1 <- multinom(response ~ log(count), data = df)
summary(mod1)

# Log-scaled
df$count_log <- log(df$count)

# Regression
mod2 <- multinom(response ~ count_log,
                 data = df,
                 Hess = TRUE)

# The models are identical:
coef(mod1) == coef(mod2)

After this step, you can use the mnl_pred_ova or the mnl_fd2_ova function for predicted probabilities or first differences/predicted marginal effects.

# 10 steps for predictions
steps <- (max(df$count_log) - min(df$count_log))/9

pred1 <- mnl_pred_ova(mod2,
                      data = df,
                      by = steps,
                      x = "count_log")

x_breaks <- seq(from = min(df$count_log),
                to = max(df$count_log), 
                length.out = 5)

x_labels <- seq(from = min(df$count),
                to = max(df$count),
                length.out = 5)

pred1$plotdata %>%
  ggplot(aes(x = count_log,
             y = mean,
             ymin = lower,
             ymax = upper)) +
  facet_wrap(. ~ response) +
  geom_line() +
  geom_ribbon(alpha = 0.2) +
  scale_y_continuous(labels = percent_format()) +
  scale_x_continuous(breaks = x_breaks,
                     labels = x_labels) +
  theme_bw()

Or the predicted marginal effects:

pred_fd <- mnl_fd2_ova(model = mod2,
                       x = "count_log",
                       value1 = min(df$count_log),
                       value2 = max(df$count_log),
                       data = df)

pred_fd$plotdata_fd %>%
  ggplot(aes(x = categories,
             y = mean,
             ymin = lower,
             ymax = upper)) +
  geom_pointrange() +
  scale_y_continuous(labels = percent_format()) +
  labs(title = "Predicted effect of Count on responses",
       x = "Categories",
       y = "Predicted marginal effect") +
  theme_bw()