This package provides functions that make it easy to get plottable predictions from multinomial logit models. The predictions are based on simulated draws of regression estimates from their respective sampling distribution.

At first I will present the theoretical and statistical background, before using sample data to demonstrate the functions of the package.

For the statistical and theoretical background of the multinomial logit regression please refer to the vignette or sources like these lecture notes by Germán Rodríguez.

Due to the inconvenience of integrating math equations in the README file, this is not the place to write comprehensively about it.

These are the important characteristics of the model:

- The multinomial logit regression is used to model nominal outcomes. It provides the opportunity to assign specific choices a probability, based on a set of independent variables.
- The model needs an assigned baseline category to be identifiable. All other choices are evaluated in contrast to this reference.
- The model returns a set of coefficients for each choice category.
- Like all logit models, the multinomial logit model returns log-odds which are difficult to interpret in terms of effect sizes and uncertainties.

This package helps to interpret the model in meaningful ways.

The package can be both installed from CRAN or the github repository:

```
# Uncomment if necessary:
# install.packages("MNLpred")
# devtools::install_github("ManuelNeumann/MNLpred")
```

As we have seen above, the multinomial logit can be used to get an insight into the probabilities to choose one option out of a set of alternatives. We have also seen that we need a baseline category to identify the model. This is mathematically necessary, but does not come in handy for purposes of interpretation.

It is far more helpful and easier to understand to come up with predicted probabilities and first differences for values of interest (see e.g., King, Tomz, and Wittenberg 2000 for approaches in social sciences). Based on simulations, this package helps to easily predict probabilities and their uncertainty in forms of confidence intervals for each choice category over a specified scenario. The functions use the observed values to compute the predicted probabilities, as is recommended by Hanmer and Ozan Kalkan (2013).

The procedure follows the following steps:

- Estimate a multinomial model and save the coefficients and the variance covariance matrix (based on the Hessian-matrix of the model).
- To simulate uncertainty, make n draws of coefficients from a simulated sampling distribution based on the coefficients and the variance covariance matrix.
- Predict probabilities by multiplying the drawn coefficients with a specified scenario (the observed values).
- Take the mean and the quantiles of the simulated predicted probabilities.

The presented functions follow these steps. Additionally, they use the so called observed value approach. This means that the “scenario” uses all observed values that informed the model. Therefore the function takes these more detailed steps:

- For all (complete) cases n predictions are computed based on their observed independent values and the n sets of coefficients.
- Next, the predicted values of all observations for each simulation are averaged.
- Take the mean and the quantiles of the simulated predicted probabilities (same as above).

For first differences, the simulated predictions are subtracted from each other.

To showcase these steps, I present a reproducible example of how the functions can be used.

The example uses data from the German Longitudinal Election Study (GLES, Roßteutscher et al. (2019)).

The contains 1,000 respondents characteristics and their vote choice.

For this task, we need the following packages:

```
# Required packages
library(magrittr) # for pipes
library(nnet) # for the multinom()-function
library(MASS) # for the multivariate normal distribution
# The package
library(MNLpred)
# Plotting the predicted probabilities:
library(ggplot2)
library(scales)
```

Now we load the data:

The next step is to compute the actual model. The function of the `MNLpred`

package is based on models that were estimated with the `multinom()`

-function of the `nnet`

package. The `multinom()`

function is convenient because it does not need transformed datasets. The syntax is very easy and resembles the ordinary regression functions. Important is that the Hessian matrix is returned with `Hess = TRUE`

. The matrix is needed to simulate the sampling distribution.

As we have seen above, we need a baseline or reference category for the model to work. Therefore, be aware what your baseline category is. If you use a dependent variable that is of type `character`

, the categories will be ordered in alphabetical order. If you have a `factor`

at hand, you can define your baseline category, for example with the `relevel()`

function.

Now, let’s estimate the model:

```
# Multinomial logit model:
mod1 <- multinom(vote ~ egoposition_immigration +
political_interest +
income + gender + ostwest,
data = gles,
Hess = TRUE)
#> # weights: 42 (30 variable)
#> initial value 1791.759469
#> iter 10 value 1644.501289
#> iter 20 value 1553.803188
#> iter 30 value 1538.792079
#> final value 1537.906674
#> converged
```

The results show the coefficients and standard errors. As we can see, there are five sets of coefficients. They describe the relationship between the reference category (`AfD`

) and the vote choices for the parties `CDU/CSU`

, `FDP`

, `Gruene`

, `LINKE`

, and `SPD`

.

```
summary(mod1)
#> Call:
#> multinom(formula = vote ~ egoposition_immigration + political_interest +
#> income + gender + ostwest, data = gles, Hess = TRUE)
#>
#> Coefficients:
#> (Intercept) egoposition_immigration political_interest income
#> CDU/CSU 3.101201 -0.4419104 -0.29177070 0.33114348
#> FDP 2.070618 -0.4106626 -0.19044703 0.18496691
#> Gruene 3.232074 -0.8482213 -0.03023454 0.24330589
#> LINKE 4.990008 -0.7477359 -0.04503371 -0.24206850
#> SPD 3.799394 -0.6425427 -0.03514426 0.08211066
#> gender ostwest
#> CDU/CSU 1.296949 0.79760035
#> FDP 1.252112 1.01378955
#> Gruene 1.831714 0.76299897
#> LINKE 1.368591 -0.02428322
#> SPD 1.497019 0.74026388
#>
#> Std. Errors:
#> (Intercept) egoposition_immigration political_interest income
#> CDU/CSU 0.8568928 0.06504100 0.1694270 0.1872533
#> FDP 0.9508589 0.07083385 0.1882974 0.2063590
#> Gruene 0.9854950 0.07887427 0.1969704 0.2149368
#> LINKE 0.9505656 0.07755359 0.1962954 0.2058036
#> SPD 0.8880256 0.06912678 0.1779570 0.1924002
#> gender ostwest
#> CDU/CSU 0.3530225 0.3120178
#> FDP 0.3794295 0.3615071
#> Gruene 0.3875116 0.3671397
#> LINKE 0.3885663 0.3493941
#> SPD 0.3625652 0.3269007
#>
#> Residual Deviance: 3075.813
#> AIC: 3135.813
```

A first rough review of the coefficients shows that a more restrictive ego-position toward immigration leads to a lower probability of the voters to choose any other party than the AfD. It is hard to evaluate whether the effect is statistically significant and how the probabilities for each choice look like. For this it is helpful to predict the probabilities for certain scenarios and plot the means and confidence intervals for visual analysis.

Let’s say we are interested in the relationship between the ego-position toward immigration and the probability to choose any of the parties. It would be helpful to plot the predicted probabilities for the span of the positions.

```
summary(gles$egoposition_immigration)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 3.000 4.000 4.361 6.000 10.000
```

As we can see, the ego positions were recorded on a scale from 0 to 10. Higher numbers represent more restrictive positions. We pick this score as the x-variable (`xvari`

) and use the `mnl_pred_ova()`

function to get predicted probabilities for each position in this range.

The function needs a multinomial logit model (`model`

), data (`data`

), the variable of interest `xvari`

, the steps for which the probabilities should be predicted (`by`

). Additionally, a `seed`

can be defined for replication purposes, the numbers of simulations can be defined (`nsim`

), and the confidence intervals (`probs`

).

If we want to hold another variable stable, we can specify so with `scennname`

and `scenvalue`

. See also the `mnl_fd_ova()`

function below.

```
pred1 <- mnl_pred_ova(model = mod1,
data = gles,
xvari = "egoposition_immigration",
by = 1,
seed = "random", # default
nsim = 100, # faster
probs = c(0.025, 0.975)) # default
#> Multiplying values with simulated estimates:
#> ================================================================================
#> Applying link function:
#> ================================================================================
#> Done!
```

The function returns a list with several elements. Most importantly, it returns a `plotdata`

data set:

```
pred1$plotdata %>% head()
#> egoposition_immigration vote mean lower upper
#> 1 0 AfD 0.002542384 0.0008824122 0.005781465
#> 2 1 AfD 0.004830211 0.0019099219 0.009858447
#> 3 2 AfD 0.008995787 0.0042242499 0.016372401
#> 4 3 AfD 0.016365730 0.0088724006 0.026530469
#> 5 4 AfD 0.028963192 0.0179605817 0.041066115
#> 6 5 AfD 0.049604901 0.0348963869 0.064858008
```

As we can see, it includes the range of the x variable, a mean, a lower, and an upper bound of the confidence interval. Concerning the choice category, the data is in a long format. This makes it easy to plot it with the `ggplot`

syntax. The choice category can now easily be used to differentiate the lines in the plot by using `linetype = vote`

in the `aes()`

. Another option is to use `facet_wrap()`

or `facet_grid()`

to differentiate the predictions:

```
ggplot(data = pred1$plotdata, aes(x = egoposition_immigration,
y = mean,
ymin = lower, ymax = upper)) +
geom_ribbon(alpha = 0.1) + # Confidence intervals
geom_line() + # Mean
facet_wrap(.~ vote, scales = "free_y", ncol = 2) +
scale_y_continuous(labels = percent_format(accuracy = 1)) + # % labels
scale_x_continuous(breaks = c(0:10)) +
theme_bw() +
labs(y = "Predicted probabilities",
x = "Ego-position toward immigration") # Always label your axes ;)
```

If we want first differences between two scenarios, we can use the function `mnl_fd2_ova()`

. The function takes similar arguments as the function above, but now the values for the scenarios of interest have to be supplied. Imagine we want to know what difference it makes to position oneself on the most tolerant or most restrictive end of the `egoposition_immigration`

scale. This can be done as follows:

```
fdif1 <- mnl_fd2_ova(model = mod1,
data = gles,
xvari = "egoposition_immigration",
value1 = min(gles$egoposition_immigration),
value2 = max(gles$egoposition_immigration),
nsim = 100)
#> Multiplying values with simulated estimates:
#> ================================================================================
#> Applying link function:
#> ================================================================================
#> Done!
```

The first differences can then be depicted in a graph.

```
ggplot(fdif1$plotdata_fd, aes(x = categories,
y = mean,
ymin = lower, ymax = upper)) +
geom_pointrange() +
geom_hline(yintercept = 0) +
scale_y_continuous(labels = percent_format()) +
theme_bw() +
labs(y = "Predicted probabilities",
x = "Party vote")
```

We are often not only interested in the static difference, but the difference across a span of values, given a difference in a second variable. This is especially helpful when we look at dummy variables. For example, we could be interested in the effect of `gender`

on the vote decision over the different ego-positions. With the `mnl_fd_ova()`

function, we can predict the probabilities for two scenarios and subtract them. The function returns the differences and the confidence intervals of the differences. The different scenarios can be held stable with `scenname`

and the `scenvalues`

. `scenvalues`

takes a vector of two numeric values. These values are held stable for the variable that is named in `scenname`

.

```
fdif2 <- mnl_fd_ova(model = mod1,
data = gles,
xvari = "egoposition_immigration",
by = 1,
scenname = "gender",
scenvalues = c(0,1),
nsim = 100)
#> First scenario:
#> Multiplying values with simulated estimates:
#> ================================================================================
#> Applying link function:
#> ================================================================================
#> Done!
#>
#> Second scenario:
#> Multiplying values with simulated estimates:
#> ================================================================================
#> Applying link function:
#> ================================================================================
#> Done!
```

As before, the function returns a list including a data set that can be used to plot the differences.

```
fdif2$plotdata_fd %>% head()
#> egoposition_immigration vote mean lower upper
#> 1 0 AfD -0.002809305 -0.006054596 -0.000907856
#> 2 1 AfD -0.005312847 -0.010745906 -0.002039326
#> 3 2 AfD -0.009835140 -0.018493328 -0.004320129
#> 4 3 AfD -0.017747651 -0.031427791 -0.008634304
#> 5 4 AfD -0.031049368 -0.051355335 -0.016791086
#> 6 5 AfD -0.052272564 -0.081004465 -0.031273690
```

Since the function calls the `mnl_pred_ova()`

function internally, it also returns the output of the two predictions in the list element `Prediction1`

and `Prediction2`

. The plot data for the predictions is already bound together row wise to easily plot the predicted probabilities.

```
ggplot(data = fdif2$plotdata, aes(x = egoposition_immigration,
y = mean,
ymin = lower, ymax = upper,
group = as.factor(gender),
linetype = as.factor(gender))) +
geom_ribbon(alpha = 0.1) +
geom_line() +
facet_wrap(. ~ vote, scales = "free_y", ncol = 2) +
scale_y_continuous(labels = percent_format(accuracy = 1)) + # % labels
scale_x_continuous(breaks = c(0:10)) +
scale_linetype_discrete(name = "Gender",
breaks = c(0, 1),
labels = c("Male", "Female")) +
theme_bw() +
labs(y = "Predicted probabilities",
x = "Ego-position toward immigration") # Always label your axes ;)
```

As we can see, the differences between `female`

and `male`

differ, depending on the party and ego-position. So let’s take a look at the differences:

```
ggplot(data = fdif2$plotdata_fd, aes(x = egoposition_immigration,
y = mean,
ymin = lower, ymax = upper)) +
geom_ribbon(alpha = 0.1) +
geom_line() +
geom_hline(yintercept = 0) +
facet_wrap(. ~ vote, ncol = 3) +
scale_y_continuous(labels = percent_format(accuracy = 1)) + # % labels
scale_x_continuous(breaks = c(0:10)) +
theme_bw() +
labs(y = "Predicted probabilities",
x = "Ego-position toward immigration") # Always label your axes ;)
```

We can see that the differences are for some parties at no point statistically significant from 0.

Multinomial logit models are important to model nominal choices. They are, however, restricted by being in need of a baseline category. Additionally, the log-character of the estimates makes it difficult to interpret them in meaningful ways. Predicting probabilities for all choices for scenarios, based on the observed data provides much more insight. The functions of this package provide easy to use functions that return data that can be used to plot predicted probabilities. The function uses a model from the `multinom()`

function and uses the observed value approach and a supplied scenario to predict values over the range of fitting values. The functions simulate sampling distributions and therefore provide meaningful confidence intervals. `mnl_pred_ova()`

can be used to predict probabilities for a certain scenario. `mnl_fd_ova()`

can be used to predict probabilities for two scenarios and their first differences.

My code is inspired by the method courses in the Political Science master’s program at the University of Mannheim(cool place, check it out!). The skeleton of the code is based on a tutorial taught by Marcel Neunhoeffer (lecture: “Advanced Quantitative Methods” by Thomas Gschwend).

Hanmer, Michael J., and Kerem Ozan Kalkan. 2013. “Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models.” *American Journal of Political Science* 57 (1): 263–77. https://doi.org/10.1111/j.1540-5907.2012.00602.x.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” *American Journal of Political Science* 44 (2): 341–55. https://doi.org/10.2307/2669316.

Roßteutscher, Sigrid, Harald Schoen, Rüdiger Schmitt-Beck, Christof Wolf, and Alexander Staudt. 2019. “Rolling Cross-Section-Wahlkampfstudie Mit Nachwahl-Panelwelle (GLES 2017).” GESIS Datenarchiv. https://doi.org/10.4232/1.13213.