The package permits the covariate effects of trinomial regression models to be represented graphically by means of a ternary plot. The aim of the plots is helping the interpretation of regression coefficients in terms of the effects that a change in regressors’ values has on the probability distribution of the dependent variable. Such changes may involve either a single regressor, or a group of them (composite changes), and the package permits both cases to be handled in a user-friendly way. Theoretical and methodological details are illustrated and discussed in Santi, Dickson, and Espa (2019), whereas a detailed illustration of the package and its features is available in Santi et al. (2022).

The package can read the results of **both categorical and
ordinal trinomial logit** regression fitted by various functions
(see the next section) and creates a `field3logit`

object
which may be represented by means of functions `gg3logit`

and
`stat_field3logit`

.

The `plot3logit`

package inherits graphical classes and
methods from the package `ggtern`

(Hamilton and Ferry 2018)
which, in turn, is based on the package `ggplot2`

(Wickham
2016).

Graphical representation based on **standard graphics**
is made available through the package `Ternary`

(Smith 2017)
by functions `plot3logit`

and `TernaryField`

, and
by the `plot`

method of `field3logit`

objects.

See the help of `field3logit`

for representing composite
effects and `multifield3logit`

for drawing multiple fields
and the presentation vignette `plot3logit-overview`

by
typing:

`vignette('plot3logit-overview', package = 'plot3logit')`

Function `field3logit`

of package `plot3logit`

can read trinomial regression estimates from the output of the following
functions:

`clm`

and`clm2`

of package`ordinal`

(ordinal logit regression);`mlogit`

of package`mlogit`

(logit regression);`multinom`

of package`nnet`

(logit regression);`polr`

of package`MASS`

(ordinal logit regression);`vgam`

and`vglm`

of package`VGAM`

(logit regression).

Moreover, explicit estimates can be passed to
`field3logit()`

. See the help of the package (type
`? 'plot3logit-package'`

) and the help of functions
`field3logit()`

and `extract3logit()`

for further
details.

Fit a trilogit model by means of package `nnet`

where the
student’s employment situation is analysed with respect to all variables
in the dataset `cross_1year`

:

```
data(cross_1year)
library(nnet)
<- multinom(employment_sit ~ ., data = cross_1year) mod0
```

The gender effect is analysed by means of a ternary plot which is
generated in two steps, however, package `plot3logit`

should
be loaded:

`library(plot3logit)`

Firstly, the vector field is computed:

`<- field3logit(mod0, 'genderFemale') field0 `

Secondly, the field is represented on a ternary plot, using either
`gg`

-graphics:

`gg3logit(field0) + stat_field3logit()`

or standard graphics:

`plot(field0)`

Hamilton, N. E., and M. Ferry. 2018. “ggtern: Ternary Diagrams Using ggplot2.” *Journal of Statistical Software,
Code Snippets* 87 (3): 1–17. https://doi.org/10.18637/jss.v087.c03.

Santi, F., M. M. Dickson, and G. Espa. 2019. “A Graphical Tool for
Interpreting Regression Coefficients of Trinomial Logit Models.” *The
American Statistician* 73 (2): 200–207. https://doi.org/10.1080/00031305.2018.1442368.

Santi, F., M. M. Dickson, G. Espa, and D. Giuliani. 2022. “plot3logit: Ternary Plots for Interpreting
Trinomial Regression Models.” *Journal of Statistical Software, Code
Snippets* 103 (1): 1–27. https://doi.org/10.18637/jss.v103.c01.

Smith, M. R. 2017. “Ternary: An r Package for Creating Ternary Plots.”
*Zenodo*.

Wickham, H. 2016. *ggplot2: Elegant
Graphics for Data Analysis*. New York: Springer-Verlag.