---
title: "2. Data management, model description and testing"
output: rmarkdown::html_vignette
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{2. Data management, model description and testing}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r label = setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, widtht = 65)
options(width = 65)
```

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.

## Data management

`mlogit` is loaded using:

```{r label = 'loading mlogit'}
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 depends on the `dfidx` package
which takes as first argument a `data.frame` and returns a
`dfidx` object, which is a `data.frame` in "long" format with a
special data frame column which contains the indexes.

### Wide format

`Train`^[Used by @BENA:BOLD:BRAD:93 and @MEIJ:ROUW:06.] is an example
  of a *wide* data set:

```{r label = 'Train data'}
data("Train", package = "mlogit")
Train$choiceid <- 1:nrow(Train)
head(Train, 3)
``` 

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 `dfidx` 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.

```{r label = 'dfidx for Train'}
Tr <- dfidx(Train, shape = "wide", varying = 4:11, sep = "_",
            idx = list(c("choiceid", "id")), idnames = c(NA, "alt"))
``` 

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):

```{r label = 'data transformation for Train'}
Tr$price <- Tr$price / 100 * 2.20371
Tr$time <- Tr$time / 60
``` 

```{r label = 'head of the transformed Train data set'}
head(Tr, 3)
``` 

An `idx` column is added to the data, which contains the three
relevant indexes: `choiceid` is the choice situation index, `alt` the
alternative index and `id` is the individual index. This column can be
extracted using the `idx` funtion:

```{r label = 'index of the transformed Train data set'}
head(idx(Tr), 3)
``` 

### Long format

`ModeCanada`,^[Used in particular by [@FORI:KOPP:93],
  @BHAT:95, @KOPP:WEN:98 and @KOPP:WEN:00.] 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:
  
```{r label = 'loading ModeCanada'}
data("ModeCanada", package = "mlogit")
head(ModeCanada)
``` 

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 `dfidx`, 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 `dfidx` 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:

```{r label = 'applying dfidx to Modecanada (1)'}
MC <- dfidx(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

```{r label = 'applying dfidx to Modecanada (2)'}
MC <- dfidx(ModeCanada, subset = noalt == 4, idx = list(NA, "alt"))
``` 

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

```{r label = 'applying dfidx to Modecanada (3)'}
MC <- dfidx(ModeCanada, subset = noalt == 4, idx = "case",
            alt.levels = c("train", "air", "bus", "car"))
``` 

Both alternative and choice situation variable can also be provided:

```{r label = 'applying dfidx to Modecanada (4)'}
MC <- dfidx(ModeCanada, subset = noalt == 4, idx = c("case", "alt"))
``` 
More simply, as the two indexes are stored in the first two columns of
the original data frame, the `idx` argument can be unset:


```{r label = 'ModeCanada without idx'}
MC <- dfidx(ModeCanada, subset = noalt == 4)
```

and the indexes can be kept as stand alone series if the `drop.index`
argument is set to `FALSE`:

```{r label = 'applying dfidx to Modecanada (5)'}
MC <- dfidx(ModeCanada, subset = noalt == 4, idx = c("case", "alt"),
            drop.index = FALSE)
head(MC)
``` 

## Model description

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:


1. alternative and choice situation specific covariates $x_{ij}$
  with generic coefficients $\beta$ and and alternative specific
  covariates $t_j$ with a generic coefficient $\nu$,
2. choice situation specific covariates $z_i$ with alternative
  specific coefficients $\gamma_j$,
3. alternative and choice situation specific covariates $w_{ij}$ with
  alternative specific coefficients $\delta_j$,
4. 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 @SWAI:LOUV:93] 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.^[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](./c4.relaxiid.html)].  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.^[See
[@ZEIL:CROIS:10] for a description of the `Formula` package.] 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).

```{r label = 'a three parts formula'}
library("Formula")
f <- Formula(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:

```{r label = 'ommission of some parts (1)'}
f2 <- Formula(choice ~ cost + ivt | income + urban)
f2 <- Formula(choice ~ cost + ivt | income + urban | 0)
``` 

```{r label = 'ommission of some parts (2)'}
f3 <- Formula(choice ~ 0 | income | 0)
f3 <- Formula(choice ~ 0 | income)
``` 

```{r label = 'ommission of some parts (3)'}
f4 <- Formula(choice ~ cost + ivt)
f4 <- Formula(choice ~ cost + ivt | 1)
f4 <- Formula(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.

```{r label = 'removing the intercept'}
f5 <- Formula(choice ~ cost | income + 0 | ivt)
f5 <- Formula(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:

```{r label = 'model.matrix method for Formula objects'}
f <- Formula(choice ~ cost | income  | ivt)
mf <- model.frame(MC, f)
head(model.matrix(mf), 4)
``` 

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.

## Testing

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
[@ZEIL:HOTH:02]: `waldtest` and `lrtest`. The Wald test is also implemented 
as `linearHypothesis` in package `car` [@FOX:WEIS:10], 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`.

```{r label = 'convenient statpval function'}
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)
}
``` 

## Bibliography
