The formula-data interface is a critical advantage of the `R`

software. It provides a practical way to describe the model to be estimated and to store data. However, the usual interface is not flexible enough to deal correctly with random utility models. Therefore, `mlogit`

provides tools to construct richer `data.frame`

s and `formula`

s.

`mlogit`

is loaded using:

`library("mlogit")`

It comes with several data sets that we’ll use to illustrate the features of the library. Data sets used for multinomial logit estimation concern some individuals, that make one or a sequential choice of one alternative among a set of mutually exclusive alternatives. The determinants of these choices are covariates that can depend on the alternative and the choice situation, only on the alternative or only on the choice situation.

To illustrate this typology of the covariates, consider the case of repeated choice of destinations for vacations by families:

- the length of the vacation, the season are choice situation specific variables,
- income, family size are individual specific variables,
- distance to destination, cost are alternative specific variables.

The unit of observation is therefore the choice situation, and it is also the individual if there is only one choice situation per individual observed, which is often the case.

Such data have therefore a specific structure that can be characterized by three indexes: the alternative, the choice situation and the individual. These three indexes will be denoted `alt`

, `chid`

and `id`

. Note that the distinction between `chid`

and `id`

is only relevant if we have repeated observations for the same individual.

Data sets can have two different shapes: a *wide* shape (one row for each choice situation) or a *long* shape (one row for each alternative and, therefore, as many rows as there are alternatives for each choice situation).

`mlogit`

deals with both format. It provides a `mlogit.data`

function that take as first argument a `data.frame`

and returns a `data.frame`

in “long” format with some supplementary information about the structure of the data.

`Train`

^{1} is an example of a *wide* data set:

```
data("Train", package = "mlogit")
head(Train, 3)
```

```
## id choiceid choice price_A time_A change_A comfort_A price_B
## 1 1 1 A 2400 150 0 1 4000
## 2 1 2 A 2400 150 0 1 3200
## 3 1 3 A 2400 115 0 1 4000
## time_B change_B comfort_B
## 1 150 0 1
## 2 130 0 1
## 3 115 0 0
```

This data set contains data about a stated preference survey in Netherlands. Each individual has responded to several (up to 16) scenarios. For every scenario, two train trips are proposed to the user, with different combinations of four attributes: `price`

(the price in cents of guilders), `time`

(travel time in minutes), `change`

(the number of changes) and `comfort`

(the class of comfort, 0, 1 or 2, 0 being the most comfortable class).

This “wide” format is suitable to store choice situation (or individual specific) variables because, in this case, they are stored only once in the data. Otherwise, it is cumbersome for alternative specific variables because there are as many columns for such variables that there are alternatives.

For such a wide data set, the `shape`

argument of `mlogit.data`

is mandatory, as its default value is `"long"`

. The alternative specific variables are indicated with the `varying`

argument which is a numeric vector that indicates their position in the data frame. This argument is then passed to `stats::reshape`

that coerced the original `data.frame`

in “long” format. Further arguments may be passed to `reshape`

. For example, as the names of the variables are of the form `price_A`

, one must add `sep = "_"`

(the default value being `"."`

). The `choice`

argument is also mandatory because the response has to be transformed in a logical value in the long format. To take the panel dimension into account, one has to add an argument `id.var`

which is the name of the individual index.

```
Tr <- mlogit.data(Train, shape = "wide", choice = "choice",
varying = 4:11, sep = "_", id.var = "id",
opposite = c("price", "comfort", "time", "change"))
```

Note the use of the `opposite`

argument for the 4 covariates: we expect negative coefficients for all of them, taking the opposite of the covariates will lead to expected positive coefficients. We next convert `price`

and `time`

in more meaningful unities, hours and euros (1 guilder was \(2.20371\) euros):

```
Tr$price <- Tr$price / 100 * 2.20371
Tr$time <- Tr$time / 60
```

`head(Tr, 3)`

```
## id choiceid choice alt price time change comfort chid
## 1.A 1 1 TRUE A -52.88904 -2.5 0 -1 1
## 1.B 1 1 FALSE B -88.14840 -2.5 0 -1 1
## 2.A 1 2 TRUE A -52.88904 -2.5 0 -1 2
```

An `index`

attribute is added to the data, which contains the three relevant indexes: `chid`

is the choice situation index, `alt`

the alternative index and `id`

is the individual index. This attribute is a `data.frame`

that can be extracted using the `index`

function.

`head(index(Tr), 3)`

```
## chid alt id
## 1.A 1 A 1
## 1.B 1 B 1
## 2.A 2 A 1
```

`ModeCanada`

,^{2} is an example of a data set in long format. It presents the choice of individuals for a transport mode for the Ontario-Quebec corridor:

```
data("ModeCanada", package = "mlogit")
head(ModeCanada)
```

```
## case alt choice dist cost ivt ovt freq income urban noalt
## 1 1 train 0 83 28.25 50 66 4 45 0 2
## 2 1 car 1 83 15.77 61 0 0 45 0 2
## 3 2 train 0 83 28.25 50 66 4 25 0 2
## 4 2 car 1 83 15.77 61 0 0 25 0 2
## 5 3 train 0 83 28.25 50 66 4 70 0 2
## 6 3 car 1 83 15.77 61 0 0 70 0 2
```

There are four transport modes (`air`

, `train`

, `bus`

and `car`

) and most of the variable are alternative specific (`cost`

for monetary cost, `ivt`

for in vehicle time, `ovt`

for out of vehicle time, `freq`

for frequency). The only choice situation specific variables are `dist`

(the distance of the trip), `income`

(household income), `urban`

(a dummy for trips which have a large city at the origin or the destination) and `noalt`

the number of available alternatives. The advantage of this shape is that there are much fewer columns than in the wide format, the caveat being that values of `dist`

, `income`

and `urban`

are repeated four times.

For data in “long” format, the `shape`

and the `choice`

arguments are no more mandatory.

To replicate published results later in the text, we’ll use only a subset of the choice situations, namely those for which the 4 alternatives are available. This can be done using the `subset`

function with the `subset`

argument set to `noalt == 4`

while estimating the model. This can also be done within `mlogit.data`

, using the `subset`

argument.

The information about the structure of the data can be explicitly indicated using choice situations and alternative indexes (respectively `case`

and `alt`

in this data set) or, in part, guessed by the `mlogit.data`

function. Here, after subsetting, we have 2779 choice situations with 4 alternatives, and the rows are ordered first by choice situation and then by alternative (`train`

, `air`

, `bus`

and `car`

in this order).

The first way to read correctly this data frame is to ignore completely the two index variables. In this case, the only supplementary argument to provide is the `alt.levels`

argument which is a character vector that contains the name of the alternatives in their order of appearance:

```
MC <- mlogit.data(ModeCanada, subset = noalt == 4,
alt.levels = c("train", "air", "bus", "car"))
```

Note that this can only be used if the data set is “balanced”, which means than the same set of alternatives is available for all choice situations. It is also possible to provide an argument `alt.var`

which indicates the name of the variable that contains the alternatives

`MC <- mlogit.data(ModeCanada , subset = noalt == 4, alt.var = "alt")`

The name of the variable that contains the information about the choice situations can be indicated using the `chid.var`

argument:

```
MC <- mlogit.data(ModeCanada, subset = noalt == 4, chid.var = "case",
alt.levels = c("train", "air", "bus", "car"))
```

Both alternative and choice situation variable can also be provided:

```
MC <- mlogit.data(ModeCanada, subset = noalt == 4, chid.var = "case",
alt.var = "alt")
```

and dropped from the data frame using the `drop.index`

argument:

```
MC <- mlogit.data(ModeCanada, subset = noalt == 4, chid.var = "case",
alt.var = "alt", drop.index = TRUE)
head(MC)
```

```
## choice dist cost ivt ovt freq income urban noalt
## 109.train 0 377 58.25 215 74 4 45 0 4
## 109.air 1 377 142.80 56 85 9 45 0 4
## 109.bus 0 377 27.52 301 63 8 45 0 4
## 109.car 0 377 71.63 262 0 0 45 0 4
## 110.train 0 377 58.25 215 74 4 70 0 4
## 110.air 1 377 142.80 56 85 9 70 0 4
```

Standard `formula`

s are not very practical to describe random utility models, as these models may use different sets of covariates. Actually, working with random utility models, one has to consider at most four sets of covariates:

- alternative and choice situation specific covariates \(x_{ij}\) with generic coefficients \(\beta\) and and alternative specific covariates \(t_j\) with a generic coefficient \(\nu\),
- choice situation specific covariates \(z_i\) with alternative specific coefficients \(\gamma_j\),
- alternative and choice situation specific covariates \(w_{ij}\) with alternative specific coefficients \(\delta_j\),
- choice situation specific covariates \(v_i\) that influence the variance of the errors.

The first three sets of covariates enter the observable part of the utility which can be written, alternative \(j\):

\[ V_{ij}=\alpha_j + \beta x_{ij} + \nu t_j + \gamma_j z_i + \delta_j w_{ij} . \]

As the absolute value of utility is irrelevant, only utility differences are useful to modelise the choice for one alternative. For two alternatives \(j\) and \(k\), we obtain:

\[ V_{ij}-V_{ik}=(\alpha_j-\alpha_k) + \beta (x_{ij}-x_{ik}) + (\gamma_j-\gamma_k) z_i + (\delta_j w_{ij} - \delta_k w_{ik}) + \nu(t_j - t_k). \]

It is clear from the previous expression that coefficients of choice situation specific variables (the intercept being one of those) should be alternative specific, otherwise they would disappear in the differentiation. Moreover, only differences of these coefficients are relevant and can be identified. For example, with three alternatives 1, 2 and 3, the three coefficients \(\gamma_1, \gamma_2, \gamma_3\) associated to a choice situation specific variable cannot be identified, but only two linear combinations of them. Therefore, one has to make a choice of normalization and the simplest one is just to set \(\gamma_1 = 0\).

Coefficients for alternative and choice situation specific variables may (or may not) be alternative specific. For example, transport time is alternative specific, but 10 mn in public transport may not have the same impact on utility than 10 mn in a car. In this case, alternative specific coefficients are relevant. Monetary cost is also alternative specific, but in this case, one can consider than 1$ is 1$ whatever it is spent for the use of a car or in public transports. In this case, a generic coefficient is relevant.

The treatment of alternative specific variables don’t differ much from the alternative and choice situation specific variables with a generic coefficient. However, if some of these variables are introduced, the \(\nu\) parameter can only be estimated in a model without intercepts to avoid perfect multicolinearity.

Individual-related heteroscedasticity (see Swait and Louviere 1993) can be addressed by writing the utility of choosing \(j\) for individual \(i\): \(U_{ij}=V_{ij} + \sigma_i \epsilon_{ij}\), where \(\epsilon\) has a variance that doesn’t depend on \(i\) and \(j\) and \(\sigma_i^2 = f(v_i)\) is a parametric function of some individual-specific covariates. Note that this specification induce choice situation heteroscedasticity, also denoted scale heterogeneity.^{3}. As the overall scale of utility is irrelevant, the utility can also be writen as: \(U_{ij}^* = U_{ij} / \sigma_i = V_{ij}/\sigma_i + \epsilon_{ij}\), i.e., with homoscedastic errors. if \(V_{ij}\) is a linear combination of covariates, the associated coefficients are then divided by \(\sigma_i\).

A logit model with only choice situation specific variables is sometimes called a *multinomial logit model*, one with only alternative specific variables a *conditional logit model* and one with both kind of variables a *mixed logit model*. This is seriously misleading: *conditional logit model* is also a logit model for longitudinal data in the statistical literature and *mixed logit* is one of the names of a logit model with random parameters. Therefore, in what follows, we’ll use the name *multinomial logit model* for the model we’ve just described whatever the nature of the explanatory variables used.

`mlogit`

package provides objects of class `mFormula`

which are built upon `Formula`

objects provided by the `Formula`

package.^{4} The `Formula`

package provides richer `formula`

s, which accept multiple responses (a feature not used here) and multiple set of covariates. It has in particular specific `model.frame`

and `model.matrix`

methods which can be used with one or several sets of covariates.

To illustrate the use of `mFormula`

objects, we use again the `ModeCanada`

data set and consider three sets of covariates that will be indicated in a three-part formula, which refers to the first three items of the four points list at start of this section.

`cost`

(monetary cost) is an alternative specific covariate with a generic coefficient (part 1),`income`

and`urban`

are choice situation specific covariates (part 2),`ivt`

(in vehicle travel time) is alternative specific and alternative specific coefficients are expected (part 3).

`f <- mFormula(choice ~ cost | income + urban | ivt)`

Some parts of the formula may be omitted when there is no ambiguity. For example, the following sets of `formula`

s are identical:

```
f2 <- mFormula(choice ~ cost + ivt | income + urban)
f2 <- mFormula(choice ~ cost + ivt | income + urban | 0)
```

```
f3 <- mFormula(choice ~ 0 | income | 0)
f3 <- mFormula(choice ~ 0 | income)
```

```
f4 <- mFormula(choice ~ cost + ivt)
f4 <- mFormula(choice ~ cost + ivt | 1)
f4 <- mFormula(choice ~ cost + ivt | 1 | 0)
```

By default, an intercept is added to the model, it can be removed by using `+ 0`

or `- 1`

in the second part.

```
f5 <- mFormula(choice ~ cost | income + 0 | ivt)
f5 <- mFormula(choice ~ cost | income - 1 | ivt)
```

`model.frame`

and `model.matrix`

methods are provided for `mFormula`

objects. The latter is of particular interest, as illustrated in the following example:

```
f <- mFormula(choice ~ cost | income | ivt)
head(model.matrix(f, MC), 4)
```

```
## air:(intercept) bus:(intercept) car:(intercept) cost
## 109.train 0 0 0 58.25
## 109.air 1 0 0 142.80
## 109.bus 0 1 0 27.52
## 109.car 0 0 1 71.63
## air:income bus:income car:income train:ivt air:ivt
## 109.train 0 0 0 215 0
## 109.air 45 0 0 0 56
## 109.bus 0 45 0 0 0
## 109.car 0 0 45 0 0
## bus:ivt car:ivt
## 109.train 0 0
## 109.air 0 0
## 109.bus 301 0
## 109.car 0 262
```

The model matrix contains \(J-1\) columns for every choice situation specific variables (`income`

and the intercept), which means that the coefficient associated to the first alternative (`air`

) is set to 0. It contains only one column for `cost`

because we want a generic coefficient for this variable. It contains \(J\) columns for `ivt`

, because it is an alternative specific variable for which we want alternative specific coefficients.

As for all models estimated by maximum likelihood, three testing procedures may be applied to test hypothesis about models fitted using `mlogit`

. The set of hypothesis tested defines two models: the unconstrained model that doesn’t take these hypothesis into account and the constrained model that impose these hypothesis.

This in turns define three principles of tests: the *Wald test*, based only on the unconstrained model, the *Lagrange multiplier test* (or *score test*), based only on the constrained model and the *likelihood ratio test*, based on the comparison of both models.

Two of these three tests are implemented in the `lmtest`

package (Zeileis and Hothorn 2002): `waldtest`

and `lrtest`

. The Wald test is also implemented as `linearHypothesis`

in package `car`

(Fox and Weisberg 2010), with a fairly different syntax. We provide special methods of `waldtest`

and `lrtest`

for `mlogit`

objects and we also provide a function for the Lagrange multiplier (or score) test called `scoretest`

.

We’ll see later that the score test is especially useful for `mlogit`

objects when one is interested in extending the basic multinomial logit model because, in this case, the unconstrained model may be difficult to estimate. For the presentation of further tests, we provide a convenient `statpval`

function which extract the statistic and the p-value from the objects returned by the testing function, which can be either of class `anova`

or `htest`

.

```
statpval <- function(x){
if (inherits(x, "anova"))
result <- as.matrix(x)[2, c("Chisq", "Pr(>Chisq)")]
if (inherits(x, "htest")) result <- c(x$statistic, x$p.value)
names(result) <- c("stat", "p-value")
round(result, 3)
}
```

Ben-Akiva, M., D. Bolduc, and M. Bradley. 1993. “Estimation of Travel Choice Models with Randomly Distributed Values of Time.” Papers 9303. Laval - Recherche en Energie. https://ideas.repec.org/p/fth/lavaen/9303.html.

Bhat, Chandra R. 1995. “A Heteroscedastic Extreme Value Model of Intercity Travel Mode Choice.” *Transportation Research Part B: Methodological* 29 (6): 471–83. http://www.sciencedirect.com/science/article/pii/0191261595000156.

Forinash, C. V., and F. S. Koppleman. 1993. “Application and Interpretation of Nested Logit Models and Intercity Mode Choice.” *Transportation Record* 1413: 98–106.

Fox, John, and Sandford Weisberg. 2010. *An R Companion to Applied Regression*. Second. Thousand Oaks CA: Sage. https://socserv.socsci.mcmaster.ca/jfox/Books/Companion.

Koppelman, Franck S., and Chieh-Hua Wen. 1998. “Alternative Nested Logit Models: Structure, Properties and Estimation.” *Transportation Research B* 32 (5): 289–98.

Koppelman, Frank S., and Chieh-Hua Wen. 2000. “The Paired Combinatorial Logit Model: Properties, Estimation and Application.” *Transportation Research Part B: Methodological* 34 (2): 75–89. http://www.sciencedirect.com/science/article/pii/S0191261599000120.

Meijer, Erik, and Jan Rouwendal. 2006. “Measuring Welfare Effects in Models with Random Coefficients.” *Journal of Applied Econometrics* 21 (2): 227–44. doi:10.1002/jae.841.

Swait, J., and J. Louviere. 1993. “The Role of the Scale Parameter in the Estimation and Use of Multinomial Logit Models.” *Journal of Marketing Research* 30.

Zeileis, Achim, and Yves Croissant. 2010. “Extended Model Formulas in R: Multiple Parts and Multiple Responses.” *Journal of Statistical Software* 34 (1): 1–13. https://www.jstatsoft.org/v34/i01/.

Zeileis, Achim, and Torsten Hothorn. 2002. “Diagnostic Checking in Regression Relationships.” *R News* 2 (3): 7–10. https://CRAN.R-project.org/doc/Rnews/.

Used by Ben-Akiva, Bolduc, and Bradley (1993) and Meijer and Rouwendal (2006).↩

Used in particular by (Forinash and Koppleman 1993), Bhat (1995), Franck S. Koppelman and Wen (1998) and Frank S. Koppelman and Wen (2000).↩

This kind of heteroscedasticity shouldn’t be confused with alternative heteroscedasticity (\(\sigma^2_j \neq \sigma^2_k\)) which is introduced in the heteroskedastic logit model described in vignette relaxing the iid hypothesis↩

See (Zeileis and Croissant 2010) for a description of the

`Formula`

package.↩