- Summarize choice data
- Model discrete choices
- Conditional logit
- Mixed logit
- Multinomial probit
- Rank-ordered logit
- Rank-ordered probit

- Truly interpret the results
- Expected probabilities
- For any alternative
- For any subpopulation
- At specific covariate levels
- Differences in probabilities of selecting alternatives
- As a covariate changes for this alternative
- As a covariate changes for another alternative
- As a covariate changes for all alternatives
- Marginal effects
- Tests and confidence intervals for everything

Finally, answers to real-world and real-research questions.

Prior to Stata 16, the nonlinearities and extra correlations in most choice models made it difficult to answer truly interesting questions. You could easily test whether a covariate was significant and positive but not measure its effect on the probability of a choice. Either you accepted answers to limited questions or you derived solutions to your specific questions and programmed them by hand.

That all changes with Stata 16. Even with complicated models such as multinomial probit or mixed logit, you can now get the answers to truly interesting questions. Your favorite restaurant introduces a new chicken entree? How does that affect demand for its other chicken entrees? Its beef entrees? Its fish entrees?

With Stata 16, answers to such questions, including tests and confidence intervals, are a simple command away.

We are consistently faced with making choices. For example:

- Individuals choose their favorite breakfast cereal,
- Companies choose whether to use TV, online, or print advertising,
- Travelers choose whether to take a car, airplane, or train.

With choice models, you can analyze relationships between such choices and variables that influence them.

Stata 16 introduces a new, unified suite of features for modeling choice data. The new commands are easy to use, and they provide the most powerful tools available for interpreting choice model results.

To get started with any choice model analysis, you first **cmset** your data, say,

.cmset id travelmode

You are now ready to summarize your choice data, fit models, and interpret the results.

Summarize:

With the new commands **cmsummarize**, **cmchoiceset**, **cmtab**, and **cmsample**, you
can explore, summarize, and look for potential problems in your choice
data.

Model:

Stata's commands for fitting choice models have been improved and renamed.
You can use the new **cm** estimation commands to fit the
following choice models:

cmclogit |
conditional logit (McFadden's choice) model |

cmmixlogit |
mixed logit model |

cmxtmixlogit |
panel-data mixed logit model |

cmmprobit |
multinomial probit model |

cmroprobit |
rank-ordered probit model |

cmrologit |
rank-ordered logit model |

**cmxtmixlogit** is another new feature of Stata 16. It fits mixed logit
models for panel data, and we tell you all about it
here.

Interpret:

Here's the most exciting part: **margins** now works after fitting
any of these choice models. This means you can now easily interpret the
results of your choice models. While the coefficients estimated in
choice models are often almost uninterpretable, **margins** allows you to
ask and answer very specific questions based on your results.

If you are modeling choice of transportation mode, you might ask questions such as

- What proportion of travelers are expected to choose air travel?
- How does the probability of traveling by car change for each additional $10,000 in income?
- If wait times at the airport increase by 30 minutes, how does this affect the choice of each mode of transportation?

**margins** provides the answers to these questions and many others.

We have data recording individuals' choices of travel method between two cities.

Contains data from travel2.dta obs: 840 vars: 7 23 Jun 2019 09:37

storage display value | ||

variable name type format label variable label | ||

chosen byte %8.0g travel mode chosen | ||

income byte %8.0g household income | ||

partysize byte %8.0g party size | ||

id int %9.0g ID code | ||

mode byte %8.0g travel travel mode | ||

time float %9.0g travel time | ||

income_cat byte %8.0g quart income quartiles | ||

Sorted by: id |

To begin our analysis of these choice data, we tell Stata that the
**id** variable identifies the cases (individuals) and the **mode**
variable identifies the alternatives (modes of travel).

.cmset id modecaseid variable: id alternatives variable: mode

Before fitting a model, let's learn a little more about our data. **cmtab**
shows us the proportion of individuals that chose each model of
transportation. The option **choice(chosen)** says that the variable named
**chosen** in our dataset records each individual's chosen travel mode.

.cmtab, choice(chosen)Tabulation of chosen alternatives (chosen = 1)

travel mode | Freq. Percent Cum. | |

air | 58 27.62 27.62 | |

train | 63 30.00 57.62 | |

bus | 30 14.29 71.90 | |

car | 59 28.10 100.00 | |

Total | 210 100.00 |

Train transportation was most frequently chosen, but just slightly more often than air or car transportation. Only 14% of individuals took a bus.

We are interested in the effect of income on the chosen travel mode. Before we fit our model, let's look at the mean income for those who selected each travel mode.

.cmsummarize income, choice(chosen)Statistics by chosen alternatives (chosen = 1) income is constant within case Summary for variables: income by categories of: _chosen_alternative (chosen = 1)

_chosen_alternative | mean | |

air | 41.72414 | |

train | 23.06349 | |

bus | 29.7 | |

car | 42.22034 | |

Total | 34.54762 |

Not surprisingly, average income is the highest among those who choose to travel by air and by car.

Now, let's fit a model and see what the results tell us about this
relationship and others. To demonstrate, we fit a conditional logistic
regression model using **cmclogit**, but the analysis that follows
could be performed after fitting any other choice model.

We will include travel time, income, and the number of individuals traveling
together (**partysize**) as covariates in our model. Travel time differs for each
mode of transportation; it is called an alternative-specific variable. Income
and party size do not vary across alternatives for an individual; they are
known as case-specific variables and are listed in the **casevars()** option.

.cmclogit chosen time, casevars(income partysize)Iteration 0: log likelihood = -249.36629 Iteration 1: log likelihood = -236.01608 Iteration 2: log likelihood = -235.65162 Iteration 3: log likelihood = -235.65065 Iteration 4: log likelihood = -235.65065 Conditional logit choice model Number of obs = 840 Case ID variable: id Number of cases = 210 Alternatives variable: mode Alts per case: min = 4 avg = 4.0 max = 4 Wald chi2(7) = 71.14 Log likelihood = -235.65065 Prob > chi2 = 0.0000

chosen | Coef. Std. Err. z P>|z| [95% Conf. Interval] | |

mode | ||

time | -.0041641 .0007588 -5.49 0.000 -.0056512 -.002677 | |

air | (base alternative) | |

train | ||

income | -.0613414 .0122637 -5.00 0.000 -.0853778 -.0373051 | |

partysize | .4123606 .2406358 1.71 0.087 -.0592769 .883998 | |

_cons | 3.39349 .6579166 5.16 0.000 2.103997 4.682982 | |

bus | ||

income | -.0363345 .0134318 -2.71 0.007 -.0626605 -.0100086 | |

partysize | -.1370778 .3437092 -0.40 0.690 -.8107354 .5365798 | |

_cons | 2.919314 .7658496 3.81 0.000 1.418276 4.420351 | |

car | ||

income | -.0096347 .0111377 -0.87 0.387 -.0314641 .0121947 | |

partysize | .7350802 .2184636 3.36 0.001 .3068993 1.163261 | |

_cons | .7471042 .6732971 1.11 0.267 -.5725338 2.066742 | |

What can we determine from these results? The coefficient on **time** is negative,
so the probability of choosing any method of travel decreases as its travel
time increases.

For the train alternative, the coefficient on **income** is negative. Because air
travel is the base alternative, this negative coefficient tells us that as
income increases, people are less likely to choose a train over an airplane.
For the car alternative, the coefficient on **partysize** is positive. As party size
increases, people are more likely to choose a car over an airplane.

But what about questions that we can't answer from this output? For instance,
what happens to the probability of selecting car travel as income
increases? This is where **margins** comes in.

Let's estimate the expected proportion of travelers who would choose car travel across income levels ranging from $30,000 to $70,000 per year.

.margins, at(income=(30(10)70)) outcome(car)Predictive margins Number of obs = 840 Model VCE : OIM Expression : Pr(mode|1 selected), predict() Outcome : car 1._at : income = 30 2._at : income = 40 3._at : income = 50 4._at : income = 60 5._at : income = 70

Delta-method | ||

Margin Std. Err. z P>|z| [95% Conf. Interval] | ||

_at | ||

1 | .2717914 .0329811 8.24 0.000 .2071497 .3364331 | |

2 | .3169817 .0329227 9.63 0.000 .2524544 .3815091 | |

3 | .3522391 .0391994 8.99 0.000 .2754097 .4290684 | |

4 | .3760093 .050679 7.42 0.000 .2766802 .4753383 | |

5 | .3889296 .0655865 5.93 0.000 .2603825 .5174768 | |

In the first line, we see that at the $30,000 income level, 27% of travelers are expected to choose car travel. But at the $70,000 income level, 39% are expected to choose car travel.

If we simply type **marginsplot**, we can easily visualize the effect of income.

We see that the expected probability of choosing car transportation increases as income increases. But are these differences statistically significant?

We can again use **margins** to answer this question. Specifying the
**contrast()** option, we can test for differences in the expected
probabilities for each $10,000 increase in income. The **atcontrast(ar)**
option requests reverse adjacent contrasts—comparisons with the
previous income level.

.margins, at(income=(30(10)70)) outcome(car) contrast(atcontrast(ar) nowald effects)Contrasts of predictive margins Number of obs = 840 Model VCE : OIM Expression : Pr(mode|1 selected), predict() Outcome : car 1._at : income = 30 2._at : income = 40 3._at : income = 50 4._at : income = 60 5._at : income = 70

Delta-method | ||

Contrast Std. Err. z P>|z| [95% Conf. Interval] | ||

_at | ||

(2 vs 1) | .0451903 .016664 2.71 0.007 .0125296 .0778511 | |

(3 vs 2) | .0352574 .017903 1.97 0.049 .0001681 .0703466 | |

(4 vs 3) | .0237702 .0190387 1.25 0.212 -.013545 .0610854 | |

(5 vs 4) | .0129204 .0200549 0.64 0.519 -.0263866 .0522273 | |

At a 5% significance level, the effect of a $10,000 increase in income
is statistically significant only when going from $30,000 to $40,000 (**2 vs 1**)
and from $40,000 to $50,000 (**3 vs 2**).

Above, we have results for car travel, but we can easily estimate similar
effects for each other travel alternative. We simply change
**outcome(car)** to **outcome(train)** or any of the other outcomes. Or
we can remove the **outcome()** option altogether and get estimates
for all outcomes. This is helpful if you want to compare effects
of income across all modes of transportation. For instance, if we type

. margins, at(income=(30(10)70)). marginsplot, noci

we see how the expected probability of selecting each transportation mode changes across income levels.

So far, we have focused on the effects of income. What about analyzing
the effect of an alternative-specific variable like **time**? We can ask
different questions for these variables. For instance,
if wait times at airports increase by an hour, how do we expect this
to affect the probability of selecting air travel? How does it
affect the probability of selecting car travel? Train travel?
Bus travel?

We find out by typing

. margins, at(time=generate(time)) at(time=generate(time+60)) alternative(air)

The first **at()** option requests expected probabilities without the additional
travel time, and the second **at()** option requests expected probabilities with an
additional 60 minutes added to air travel time.

Delta-method | ||

Margin Std. Err. z P>|z| [95% Conf. Interval | ||

_outcome#_at | ||

air#1 | .2761905 .0275268 10.03 0.000 .2222389 .330142 | |

air#2 | .2379693 .0258772 9.20 0.000 .1872509 .2886878 | |

train#1 | .3 .0284836 10.53 0.000 .2441731 .3558269 | |

train#2 | .3134023 .0293577 10.68 0.000 .2558623 .3709424 | |

bus#1 | .1428571 .0234186 6.10 0.000 .0969576 .1887567 | |

bus#2 | .1514859 .024598 6.16 0.000 .1032747 .1996972 | |

car#1 | .2809524 .028043 10.02 0.000 .2259891 .3359156 | |

car#2 | .2971424 .0289281 10.27 0.000 .2404443 .3538405 | |

Not surprisingly, we see a decrease in the expected probability of traveling by air from 0.276 to 0.238. The expected probabilities of selecting any of the other travel modes increase slightly.

Again, we can use **marginsplot** to visualize the effect of this change in
air travel time.

Here we have given you only a taste of the types of analyses you can now perform using **margins**.

For more about inference using **margins**, see [CM] Intro 1.

Learn more about Stata's choice model features.

Read more about Stata's commands for choice models in the
*Stata Choice Models Reference Manual*.