mlogit package: estimation of random utility discrete choice models by maximum likelihood

Description

mlogit provides a model description interface (enhanced formula-data), a very versatile estimation function and a testing infrastructure to deal with random utility models.

Details

For a gentle and comprehensive introduction to the package, see the package's vignettes.

Croissant Y (2020). “Estimation of Random Utility Models in R: The mlogit Package".” Journal of Statistical Software, 95(11), 1–41. doi: 10.18637/jss.v095.i11.

Train K (2009). Discrete Choice Methods with Simulation. Cambridge University Press. https://EconPapers.repec.org/RePEc:cup:cbooks:9780521766555.

Stated Preferences for Car Choice

Description

a sample of 4654 individuals

Format

A dataframe containing :

• choice: choice of a vehicule amoung 6 propositions,

• college: college education?,

• hsg2: size of household greater than 2?

• coml5: commulte lower than 5 miles a day?,

• typez: body type, one of regcar (regular car), sportuv (sport utility vehicule), sportcar, stwagon (station wagon), truck, van, for each proposition z from 1 to 6,

• fuelz: fuel for proposition z, one of gasoline, methanol, cng (compressed natural gas), electric.,

• pricez: price of vehicule divided by the logarithme of income,

• rangez: hundreds of miles vehicule can travel between refuelings/rechargings,

• accz: acceleration, tens of seconds required to reach 30 mph from stop,

• speedz: highest attainable speed in hundreds of mph,

• pollutionz: tailpipe emissions as fraction of those for new gas vehicule,

• sizez: 0 for a mini, 1 for a subcompact, 2 for a compact and 3 for a mid–size or large vehicule,

• spacez: fraction of luggage space in comparable new gas vehicule,

• costz: cost per mile of travel (tens of cents) : home recharging for electric vehicule, station refueling otherwise,

• stationz: fraction of stations that can refuel/recharge vehicule.

Source

Journal of Applied Econometrics data archive.

References

McFadden D, Train K (2000). “Mixed MNL Models for Discrete Response.” Journal of Applied Econometrics, 15(5), 447–470. ISSN 08837252, 10991255.

Choice of Brand for Catsup

Description

a sample of 2798 individuals

Format

A dataframe containing :

• id: individuals identifiers,

• choice: one of heinz41, heinz32, heinz28, hunts32,

• disp.z: is there a display for brand z ?

• feat.z: is there a newspaper feature advertisement for brand z?

• price.z: price of brand z.

Source

Journal of Business Economics and Statistics web site.

References

Jain DC, Vilcassim NJ, Chintagunta PK (1994). “A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data.” Journal of Business \& Economic Statistics, 12(3), 317-328.

Correlation structure of the random parameters

Description

Functions that extract the correlation structure of a mlogit object

Usage

cor.mlogit(x)

cov.mlogit(x)

Arguments

Details

These functions are deprecated, use vcov. instead.

Value

A numerical matrix which returns either the correlation or the covariance matrix of the random parameters.

Author(s)

Yves Croissant

Choice of Brand for Crakers

Description

a sample of 3292 individualscross-section

Format

A dataframe containing :

• id: individuals identifiers,

• choice: one of sunshine, keebler, nabisco, private,

• disp.z: is there a display for brand z?

• feat.z: is there a newspaper feature advertisement for brand z?

• price.z: price of brand z.

Source

Journal of Business Economics and Statistics web site.

References

Jain DC, Vilcassim NJ, Chintagunta PK (1994). “A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data.” Journal of Business \& Economic Statistics, 12(3), 317-328.

Paap R, Franses PH (2000). “A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables.” Journal of Applied Econometrics, 15(6), 717-744.

Functions used to describe the characteristics of estimated random parameters

Description

Functions used to describe the characteristics of estimated random parameters

Usage

stdev(x, ...)

rg(x, ...)

med(x, ...)

## S3 method for class 'rpar'
mean(x, norm = NULL, ...)

## S3 method for class 'rpar'
med(x, norm = NULL, ...)

## S3 method for class 'rpar'
stdev(x, norm = NULL, ...)

## S3 method for class 'rpar'
rg(x, norm = NULL, ...)

## S3 method for class 'mlogit'
mean(x, par = NULL, norm = NULL, ...)

## S3 method for class 'mlogit'
med(x, par = NULL, norm = NULL, ...)

## S3 method for class 'mlogit'
stdev(x, par = NULL, norm = NULL, ...)

## S3 method for class 'mlogit'
rg(x, par = NULL, norm = NULL, ...)

qrpar(x, ...)

prpar(x, ...)

drpar(x, ...)

## S3 method for class 'rpar'
qrpar(x, norm = NULL, ...)

## S3 method for class 'rpar'
prpar(x, norm = NULL, ...)

## S3 method for class 'rpar'
drpar(x, norm = NULL, ...)

## S3 method for class 'mlogit'
qrpar(x, par = 1, y = NULL, norm = NULL, ...)

## S3 method for class 'mlogit'
prpar(x, par = 1, y = NULL, norm = NULL, ...)

## S3 method for class 'mlogit'
drpar(x, par = 1, y = NULL, norm = NULL, ...)

Arguments

Details

rpar objects contain all the relevant information about the distribution of random parameters. These functions enables to obtain easily descriptive statistics, density, probability and quantiles of the distribution.

mean, med, stdev and rg compute respectively the mean, the median, the standard deviation and the range of the random parameter. qrpar, prpar, drpar return functions that compute the quantiles, the probability and the density of the random parameters (note that sd and range are not generic function in R and that median is, but without ...).

Value

a numeric vector for qrpar, drpar and prpar, a numeric vector for mean, stdev and med and a numeric matrix for rg.

Author(s)

Yves Croissant

See Also

mlogit() for the estimation of random parameters logit models and rpar() for the description of rpar objects.

Marginal effects of the covariates

Description

The effects method for mlogit objects computes the marginal effects of the selected covariate on the probabilities of choosing the alternatives

Usage

## S3 method for class 'mlogit'
effects(
  object,
  covariate = NULL,
  type = c("aa", "ar", "rr", "ra"),
  data = NULL,
  ...
)

Arguments

Value

If the covariate is alternative specific, a J \times J matrix is returned, J being the number of alternatives. Each line contains the marginal effects of the covariate of one alternative on the probability to choose any alternative. If the covariate is individual specific, a vector of length J is returned.

Author(s)

Yves Croissant

See Also

mlogit() for the estimation of multinomial logit models.

Examples

data("Fishing", package = "mlogit")
library("zoo")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
m <- mlogit(mode ~ price | income | catch, data = Fish)
# compute a data.frame containing the mean value of the covariates in
# the sample
z <- with(Fish, data.frame(price = tapply(price, idx(m, 2), mean),
                           catch = tapply(catch, idx(m, 2), mean),
                           income = mean(income)))
# compute the marginal effects (the second one is an elasticity
## IGNORE_RDIFF_BEGIN
effects(m, covariate = "income", data = z)
## IGNORE_RDIFF_END
effects(m, covariate = "price", type = "rr", data = z)
effects(m, covariate = "catch", type = "ar", data = z)

Stated preference data for the choice of electricity suppliers

Description

A sample of 2308 households in the United States

Format

A dataframe containing :

• choice: the choice of the individual, one of 1, 2, 3, 4,

• id: the individual index,

• pfi: fixed price at a stated cents per kWh, with the price varying over suppliers and experiments, for scenario i=(1, 2, 3, 4),

• cli: the length of contract that the supplier offered, in years (such as 1 year or 5 years.) During this contract period, the supplier guaranteed the prices and the buyer would have to pay a penalty if he/she switched to another supplier. The supplier could offer no contract in which case either side could stop the agreement at any time. This is recorded as a contract length of 0,

• loci: is the supplier a local company,

• wki: is the supplier a well-known company,

• todi: a time-of-day rate under which the price is 11 cents per kWh from 8am to 8pm and 5 cents per kWh from 8pm to 8am. These TOD prices did not vary over suppliers or experiments: whenever the supplier was said to offer TOD, the prices were stated as above.

• seasi: a seasonal rate under which the price is 10 cents per kWh in the summer, 8 cents per kWh in the winter, and 6 cents per kWh in the spring and fall. Like TOD rates, these prices did not vary. Note that the price is for the electricity only, not transmission and distribution, which is supplied by the local regulated utility.

Source

Kenneth Train's home page.

References

Huber J, Train K (2000). “On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths.” Marketing Letters, 12, 259–269.

Revelt D, Train K (2001). “Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier.” Econometrics 0012001, University Library of Munich, Germany. https://ideas.repec.org/p/wpa/wuwpem/0012001.html.

Choice of Fishing Mode

Description

A sample of 1182 individuals in the United-States for the choice of 4 alternative fishing modes.

Format

A dataframe containing :

• mode: recreation mode choice, one of : beach, pier, boat and charter,

• price.beach: price for beach mode

• price.pier: price for pier mode,

• price.boat: price for private boat mode,

• price.charter: price for charter boat mode,

• catch.beach: catch rate for beach mode,

• catch.pier: catch rate for pier mode,

• catch.boat: catch rate for private boat mode,

• catch.charter: catch rate for charter boat mode,

• income: monthly income,

Source

Cameron A, Trivedi P (2005). Microeconometrics. Cambridge University Press. https://EconPapers.repec.org/RePEc:cup:cbooks:9780521848053.

References

Herriges JA, Kling CL (1999). “Nonlinear Income Effects in Random Utility Models.” The Review of Economics and Statistics, 81(1), 62-72. doi: 10.1162/003465399767923827, https://doi.org/10.1162/003465399767923827 , https://doi.org/10.1162/003465399767923827.

Ranked data for gaming platforms

Description

A sample of 91 Dutch individuals

Format

A dataframe containing :

• ch.Platform: where platform is one of Xbox, PlayStation, PSPortable, GameCube, GameBoy and PC. This variables contain the ranking of the platforms from 1 to 6,

• own.Platform: these 6 variables are dummies which indicate whether the given plaform is already owned by the respondent,

• age: the age of the respondent,

• hours: hours per week spent on gaming.,

Details

The data are also provided in long format (use in this case data(Game2). In this case, the alternative and the choice situation are respectively indicated in the platform and chid variables.

Source

Journal of Applied Econometrics data archive.

References

Fok D, Paap R, Van Dijk B (2012). “A Rank-Ordered Logit Model With Unobserved Heterogeneity In Ranking Capatibilities.” Journal of Applied Econometrics, 27(5), 831-846. doi: 10.1002/jae.1223, https://onlinelibrary.wiley.com/doi/pdf/10.1002/jae.1223, https://onlinelibrary.wiley.com/doi/abs/10.1002/jae.1223.

Indicates whether the formula contains an intercept

Description

This is a generic which provide convenient methods for formula/Formula object and for specific fitted models

Usage

has.intercept(object, ...)

## Default S3 method:
has.intercept(object, ...)

## S3 method for class 'formula'
has.intercept(object, ...)

## S3 method for class 'Formula'
has.intercept(object, rhs = NULL, ...)

## S3 method for class 'mlogit'
has.intercept(object, ...)

Arguments

Author(s)

Yves Croissant

Heating and Cooling System Choice in Newly Built Houses in California

Description

A sample of 250 Californian households

Format

A dataframe containing :

• depvar: heating system, one of gcc (gas central heat with cooling), ecc (electric central resistence heat with cooling), erc (electric room resistence heat with cooling), hpc (electric heat pump which provides cooling also), gc (gas central heat without cooling), ec (electric central resistence heat without cooling), er (electric room resistence heat without cooling),

• ich.z: installation cost of the heating portion of the system,

• icca: installation cost for cooling,

• och.z: operating cost for the heating portion of the system,

• occa: operating cost for cooling,

• income: annual income of the household.

Source

Kenneth Train's home page.

Heating System Choice in California Houses

Description

A sample of 900 Californian households#'

Format

A dataframe containing:

• idcase: id,

• depvar: heating system, one of gc (gas central), gr (gas room), ec (electric central), er (electric room), hp (heat pump),

• ic.z: installation cost for heating system z (defined for the 5 heating systems),

• oc.z: annual operating cost for heating system z (defined for the 5 heating systems),

• pb.z: ratio oc.z/ic.z ,

• income: annual income of the household,

• agehed: age of the household head

• rooms: numbers of rooms in the house,

Source

Kenneth Train's home page.

Hausman-McFadden Test

Description

Test the IIA hypothesis (independence of irrelevant alternatives) for a multinomial logit model.

Usage

hmftest(x, ...)

## S3 method for class 'formula'
hmftest(x, alt.subset, ...)

## S3 method for class 'mlogit'
hmftest(x, z, ...)

Arguments

Details

This is an implementation of the Hausman's consistency test for multinomial logit models. If the independance of irrelevant alternatives applies, the probability ratio of every two alternatives depends only on the characteristics of these alternatives. Consequentely, the results obtained on the estimation with all the alternatives or only on a subset of them are consistent, but more efficient in the first case. On the contrary, only the results obtained from the estimation on a relevant subset are consistent. To compute this test, one needs a model estimated with all the alternatives and one model estimated on a subset of alternatives. This can be done by providing two objects of class mlogit, one object of class mlogit and a character vector indicating the subset of alternatives, or a formula and a subset of alternatives.

Value

an object of class "htest".

Author(s)

Yves Croissant

References

Hausman, J.A. and D. McFadden (1984), A Specification Test for the Multinomial Logit Model, Econometrica, 52, pp.1219–1240.

Examples

## from Greene's Econometric Analysis p. 731

data("TravelMode", package = "AER")
TravelMode <- mlogit.data(TravelMode, choice = "choice", shape = "long",
                          alt.var = "mode", chid.var = "individual",
                          drop.index = FALSE)

## Create a variable of income only for the air mode

TravelMode$avinc <- with(TravelMode, (mode == 'air') * income)

## Estimate the model on all alternatives, with car as the base level
## like in Greene's book.

x <- mlogit(choice ~ wait + gcost + avinc, TravelMode, reflevel = "car")

## Estimate the same model for ground modes only (the variable avinc
## must be dropped because it is 0 for every observation

g <- mlogit(choice ~ wait + gcost, TravelMode, reflevel = "car",
            alt.subset = c("car", "bus", "train"))

## Compute the test

hmftest(x,g)

Japanese Foreign Direct Investment in European Regions

Description

A sample of 452 Japanese production units in Europe #'

Format

A dataframe containing :

• firm: the investment id,

• country: the country,

• region: the region (nuts1 nomenclature),

• choice: a dummy indicating the chosen region ,

• choice.c: the chosen country,

• wage: wage rate in the region,

• unemp: unemployment rate in the region,

• elig: is the country eligible to european subsidies,

• area: the area of the region,

• scrate: social charge rate (country level),

• ctaxrate: corporate tax rate (country level),

• gdp: regional gdp,

• harris: harris' market potential,

• krugman: krugman's market potential,

• domind: domestic industry count,

• japind: japan industry count,

• network: network count.

Source

kindly provided by Thierry Mayer

References

Head K, Mayer T (2004). “Market Potential and the Location of Japanese Investment in the European Union.” The Review of Economics and Statistics, 86(4), 959-972. doi: 10.1162/0034653043125257, https://doi.org/10.1162/0034653043125257 , https://doi.org/10.1162/0034653043125257.

Compute the log-sum or inclusive value/utility

Description

The logsum function computes the inclusive value, or inclusive utility, which is used to compute the surplus and to estimate the two steps nested logit model.

Usage

logsum(
  coef,
  X = NULL,
  formula = NULL,
  data = NULL,
  type = NULL,
  output = c("chid", "obs")
)

Arguments

Details

The inclusive value, or inclusive utility, or log-sum is the log of the denominator of the probabilities of the multinomial logit model. If a "group" variable is provided in the "mlogit.data" function, the denominator can either be the one of the multinomial model or those of the lower model of the nested logit model.

If only one argument (coef) is provided, it should a mlogit object and in this case, the coefficients and the model.matrix are extracted from this model.

In order to provide a different model.matrix, further arguments could be used. X is a matrix or a mlogit from which the model.matrix is extracted. The formula-data interface can also be used to construct the relevant model.matrix.

Value

either a vector or a matrix.

Author(s)

Yves Croissant

See Also

mlogit() for the estimation of a multinomial logit model.

Methods for mlogit objects

Description

Miscellaneous methods for mlogit objects.

Usage

## S3 method for class 'mlogit'
residuals(object, outcome = TRUE, ...)

## S3 method for class 'mlogit'
df.residual(object, ...)

## S3 method for class 'mlogit'
terms(x, ...)

## S3 method for class 'mlogit'
model.matrix(object, ...)

model.response.mlogit(object, ...)

## S3 method for class 'mlogit'
update(object, new, ...)

## S3 method for class 'mlogit'
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

## S3 method for class 'mlogit'
logLik(object, ...)

## S3 method for class 'mlogit'
summary(object, ..., type = c("chol", "cov", "cor"))

## S3 method for class 'summary.mlogit'
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

## S3 method for class 'mlogit'
idx(x, n = NULL, m = NULL)

## S3 method for class 'mlogit'
idx_name(x, n = NULL, m = NULL)

## S3 method for class 'mlogit'
predict(object, newdata = NULL, returnData = FALSE, ...)

## S3 method for class 'mlogit'
fitted(
  object,
  type = c("outcome", "probabilities", "linpred", "parameters"),
  outcome = NULL,
  ...
)

## S3 method for class 'mlogit'
coef(
  object,
  subset = c("all", "iv", "sig", "sd", "sp", "chol"),
  fixed = FALSE,
  ...
)

## S3 method for class 'summary.mlogit'
coef(object, ...)

Arguments

Multinomial logit model

Description

Estimation by maximum likelihood of the multinomial logit model, with alternative-specific and/or individual specific variables.

Usage

mlogit(
  formula,
  data,
  subset,
  weights,
  na.action,
  start = NULL,
  alt.subset = NULL,
  reflevel = NULL,
  nests = NULL,
  un.nest.el = FALSE,
  unscaled = FALSE,
  heterosc = FALSE,
  rpar = NULL,
  probit = FALSE,
  R = 40,
  correlation = FALSE,
  halton = NULL,
  random.nb = NULL,
  panel = FALSE,
  estimate = TRUE,
  seed = 10,
  ...
)

Arguments

Details

For how to use the formula argument, see Formula().

The data argument may be an ordinary data.frame. In this case, some supplementary arguments should be provided and are passed to mlogit.data(). Note that it is not necessary to indicate the choice argument as it is deduced from the formula.

The model is estimated using the mlogit.optim(). function.

The basic multinomial logit model and three important extentions of this model may be estimated.

If heterosc=TRUE, the heteroscedastic logit model is estimated. J - 1 extra coefficients are estimated that represent the scale parameter for J - 1 alternatives, the scale parameter for the reference alternative being normalized to 1. The probabilities don't have a closed form, they are estimated using a gaussian quadrature method.

If nests is not NULL, the nested logit model is estimated.

If rpar is not NULL, the random parameter model is estimated. The probabilities are approximated using simulations with R draws and halton sequences are used if halton is not NULL. Pseudo-random numbers are drawns from a standard normal and the relevant transformations are performed to obtain numbers drawns from a normal, log-normal, censored-normal or uniform distribution. If correlation = TRUE, the correlation between the random parameters are taken into account by estimating the components of the cholesky decomposition of the covariance matrix. With G random parameters, without correlation G standard deviations are estimated, with correlation G * (G + 1) /2 coefficients are estimated.

Value

An object of class "mlogit", a list with elements:

• coefficients: the named vector of coefficients,

• logLik: the value of the log-likelihood,

• hessian: the hessian of the log-likelihood at convergence,

• gradient: the gradient of the log-likelihood at convergence,

• call: the matched call,

• est.stat: some information about the estimation (time used, optimisation method),

• freq: the frequency of choice,

• residuals: the residuals,

• fitted.values: the fitted values,

• formula: the formula (a Formula object),

• expanded.formula: the formula (a formula object),

• model: the model frame used,

• index: the index of the choice and of the alternatives.

Author(s)

Yves Croissant

References

McFadden D (1973). “Conditional Logit Analysis of Qualitative Choice Behaviour.” In Zarembka P (ed.), Frontiers in Econometrics, 105-142. Academic Press New York, New York, NY, USA.

McFadden D (1974). “The measurement of urban travel demand.” Journal of Public Economics, 3(4), 303 - 328. ISSN 0047-2727, https://www.sciencedirect.com/science/article/pii/0047272774900036.

Train K (2009). Discrete Choice Methods with Simulation. Cambridge University Press. https://EconPapers.repec.org/RePEc:cup:cbooks:9780521766555.

See Also

mlogit.data() to shape the data. nnet::multinom() from package nnet performs the estimation of the multinomial logit model with individual specific variables. mlogit.optim() details about the optimization function.

Examples

## Cameron and Trivedi's Microeconometrics p.493 There are two
## alternative specific variables : price and catch one individual
## specific variable (income) and four fishing mode : beach, pier, boat,
## charter

data("Fishing", package = "mlogit")
Fish <- dfidx(Fishing, varying = 2:9, shape = "wide", choice = "mode")

## a pure "conditional" model
summary(mlogit(mode ~ price + catch, data = Fish))

## a pure "multinomial model"
summary(mlogit(mode ~ 0 | income, data = Fish))

## which can also be estimated using multinom (package nnet)
summary(nnet::multinom(mode ~ income, data = Fishing))

## a "mixed" model
m <- mlogit(mode ~ price + catch | income, data = Fish)
summary(m)

## same model with charter as the reference level
m <- mlogit(mode ~ price + catch | income, data = Fish, reflevel = "charter")

## same model with a subset of alternatives : charter, pier, beach
m <- mlogit(mode ~ price + catch | income, data = Fish,
            alt.subset = c("charter", "pier", "beach"))

## model on unbalanced data i.e. for some observations, some
## alternatives are missing
# a data.frame in wide format with two missing prices
Fishing2 <- Fishing
Fishing2[1, "price.pier"] <- Fishing2[3, "price.beach"] <- NA
mlogit(mode ~ price + catch | income, Fishing2, shape = "wide", varying = 2:9)

# a data.frame in long format with three missing lines
data("TravelMode", package = "AER")
Tr2 <- TravelMode[-c(2, 7, 9),]
mlogit(choice ~ wait + gcost | income + size, Tr2)

## An heteroscedastic logit model
data("TravelMode", package = "AER")
hl <- mlogit(choice ~ wait + travel + vcost, TravelMode, heterosc = TRUE)

## A nested logit model
TravelMode$avincome <- with(TravelMode, income * (mode == "air"))
TravelMode$time <- with(TravelMode, travel + wait)/60
TravelMode$timeair <- with(TravelMode, time * I(mode == "air"))
TravelMode$income <- with(TravelMode, income / 10)
# Hensher and Greene (2002), table 1 p.8-9 model 5
TravelMode$incomeother <- with(TravelMode, ifelse(mode %in% c('air', 'car'), income, 0))
nl <- mlogit(choice ~ gcost + wait + incomeother, TravelMode,
             nests = list(public = c('train', 'bus'), other = c('car','air')))
             
# same with a comon nest elasticity (model 1)
nl2 <- update(nl, un.nest.el = TRUE)

## a probit model
## Not run: 
pr <- mlogit(choice ~ wait + travel + vcost, TravelMode, probit = TRUE)

## End(Not run)

## a mixed logit model
## Not run: 
rpl <- mlogit(mode ~ price + catch | income, Fishing, varying = 2:9,
              rpar = c(price= 'n', catch = 'n'), correlation = TRUE,
              alton = NA, R = 50)
summary(rpl)
rpar(rpl)
cor.mlogit(rpl)
cov.mlogit(rpl)
rpar(rpl, "catch")
summary(rpar(rpl, "catch"))

## End(Not run)

# a ranked ordered model
data("Game", package = "mlogit")
g <- mlogit(ch ~ own | hours, Game, varying = 1:12, ranked = TRUE,
            reflevel = "PC", idnames = c("chid", "alt"))

Some deprecated functions, especially mlogit.data, index and mFormula

Description

mlogit.data is deprecated, use dfidx::dfidx() instead, mFormula is replaced by Formula::Formula() and zoo::index() by idx.

Usage

mlogit.data(
  data,
  choice = NULL,
  shape = c("long", "wide"),
  varying = NULL,
  sep = ".",
  alt.var = NULL,
  chid.var = NULL,
  alt.levels = NULL,
  id.var = NULL,
  group.var = NULL,
  opposite = NULL,
  drop.index = FALSE,
  ranked = FALSE,
  subset = NULL,
  ...
)

mFormula(object)

## S3 method for class 'formula'
mFormula(object)

## Default S3 method:
mFormula(object)

## S3 method for class 'mFormula'
model.matrix(object, data, ...)

is.mFormula(object)

## S3 method for class 'dfidx'
index(x, ...)

## S3 method for class 'mlogit'
index(x, ...)

Arguments

Value

mlogit.data now returns a dfidx object, mFormula simply calls Formula::Formula() and returns a Formula object.

Author(s)

Yves Croissant

See Also

stats::reshape()

Non-linear minimization routine

Description

This function performs efficiently the optimization of the likelihood functions for multinomial logit models

Usage

mlogit.optim(
  logLik,
  start,
  method = c("bfgs", "nr", "bhhh"),
  iterlim = 2000,
  tol = 1e-06,
  ftol = 1e-08,
  steptol = 1e-10,
  print.level = 0,
  constPar = NULL,
  ...
)

Arguments

Details

The optimization is performed by updating, at each iteration, the vector of parameters by the amount step * direction, where step is a positive scalar and direction = H^-1 * g, where g is the gradient and H^-1 is an estimation of the inverse of the hessian. The choice of H^-1 depends on the method chosen :

if method = 'nr', H is the hessian (i.e. is the second derivates matrix of the likelihood function),

if method = 'bhhh', H is the outer-product of the individual contributions of each individual to the gradient,

if method = 'bfgs', H^-1 is updated at each iteration using a formula that uses the variations of the vector of parameters and the gradient. The initial value of the matrix is the inverse of the outer-product of the gradient (i.e. the bhhh estimator of the hessian).

The initial step is 1 and, if the new value of the function is less than the previous value, it is divided by two, until a higher value is obtained.

The routine stops when the gradient is sufficiently close to 0. The criteria is g * H^-1 * g which is compared to the tol argument. It also may stops if the number of iterations equals iterlim.

The function f has a initial.value argument which is the initial value of the likelihood. The function is then evaluated a first time with a step equals to one. If the value is lower than the initial value, the step is divided by two until the likelihood increases. The gradient is then computed and the function returns as attributes the gradient is the step. This method is more efficient than other functions available for R:

For the optim and the maxLik functions, the function and the gradient should be provided as separate functions. But, for multinomial logit models, both depends on the probabilities which are the most time-consuming elements of the model to compute.

For the nlm function, the fonction returns the gradient as an attribute. The gradient is therefore computed at each iteration, even when the function is computed with a step that is unable to increase the value of the likelihood.

Previous versions of mlogit depended on the 'maxLik' package. We kept the same interface, namely the start, method, iterlim, tol, print.level and constPar arguments.

The default method is 'bfgs', which is known to perform well, even if the likelihood function is not well behaved and the default value for print.level = 1, which means moderate printing.

A special default behavior is performed if a simple multinomial logit model is estimated. Indeed, for this model, the likelihood function is concave, the analytical hessian is simple to write and the optimization is straightforward. Therefore, in this case, the default method is 'nr' and print.level = 0.

Value

a list that contains the followings elements :

• optimum: the value of the function at the optimum, with attributes: gradi a matrix that contains the contribution of each individual to the gradient, gradient the gradient and, if ⁠method = 'nr', ⁠hessian' the hessian,

• coefficients: the vector of the parameters at the optimum,

• est.stat: a list that contains some information about the optimization : 'nb.iter' the number of iterations, 'eps' the value of the stoping criteria, 'method' the method of optimization method used, ''message'

Author(s)

Yves Croissant

Mode Choice

Description

A sample of 453 individuals for 4 transport modes.

Format

A dataframe containing :

• choice: one of car, carpool, bus or rail,

• cost.z: cost of mode z,

• time.z: time of mode z.

Source

Kenneth Train's home page.

Mode Choice for the Montreal-Toronto Corridor

Description

A sample of 3880 travellers for the Montreal-Toronto corridor

Format

A dataframe containing

• case: the individual index,

• alt: the alternative, one of train, car, bus and air,

• choice: one if the mode is chosen, zero otherwise,

• cost: monetary cost,

• ivt: in vehicule time,

• ovt: out vehicule time,

• frequency: frequency,

• income: income,

• urban: urban,

• noalt: the number of alternatives available.

Source

kindly provided by S. Koppelman

References

Bhat CR (1995). “A heteroscedastic extreme value model of intercity travel mode choice.” Transportation Research Part B: Methodological, 29(6), 471 - 483. ISSN 0191-2615, https://www.sciencedirect.com/science/article/pii/0191261595000156.

Koppelman FS, Wen C (2000). “The paired combinatorial logit model: properties, estimation and application.” Transportation Research Part B: Methodological, 34(2), 75 - 89. ISSN 0191-2615, https://www.sciencedirect.com/science/article/pii/S0191261599000120.

Wen C, Koppelman FS (2001). “The generalized nested logit model.” Transportation Research Part B: Methodological, 35(7), 627 - 641. ISSN 0191-2615, https://www.sciencedirect.com/science/article/pii/S019126150000045X.

Examples

data("ModeCanada", package = "mlogit")
bususers <- with(ModeCanada, case[choice == 1 & alt == "bus"])
ModeCanada <- subset(ModeCanada, ! case %in% bususers)
ModeCanada <- subset(ModeCanada, noalt == 4)
ModeCanada <- subset(ModeCanada, alt != "bus")
ModeCanada$alt <- ModeCanada$alt[drop = TRUE]
KoppWen00 <- mlogit.data(ModeCanada, shape='long', chid.var = 'case',
                         alt.var = 'alt', choice = 'choice',
                         drop.index = TRUE)
pcl <- mlogit(choice ~ freq + cost + ivt + ovt, KoppWen00, reflevel = 'car',
              nests = 'pcl', constPar = c('iv:train.air'))

Compute the model matrix for RUM

Description

specific stuff compared to the model.matrix.dfidx method which simply applies the Formula method

Usage

## S3 method for class 'dfidx_mlogit'
model.matrix(object, ..., lhs = NULL, rhs = 1, dot = "separate")

Arguments

Author(s)

Yves Croissant

Technologies to reduce NOx emissions

Description

A sample of 632 American production units

Format

A dataframe containing:

• chid: the plant id,

• alt: the alternative,

• id: the owner id,

• choice: the chosen alternative,

• available: a dummy indicating that the alternative is available,

• env: the regulatory environment, one of 'regulated', 'deregulated' and 'public',

• post: dummy for post-combustion polution control technology,

• cm: dummy for combustion modification technology,

• lnb: dummy for low NOx burners technology,

• age: age of the plant (in deviation from the mean age).,

• vcost: variable cost,

• kcost: capital cost.

Source

American Economic Association data archive.

References

Fowlie M (2010). “Emissions Trading, Electricity Restructuring, and Investment in Pollution Abatement.” American Economic Review, 100(3), 837-69. doi: 10.1257/aer.100.3.837, https://www.aeaweb.org/articles?id=10.1257/aer.100.3.837.

Plot of the distribution of estimated random parameters

Description

Methods for rpar and mlogit objects which provide a plot of the distribution of one or all of the estimated random parameters

Usage

## S3 method for class 'mlogit'
plot(x, par = NULL, norm = NULL, type = c("density", "probability"), ...)

## S3 method for class 'rpar'
plot(x, norm = NULL, type = c("density", "probability"), ...)

Arguments

Details

For the rpar method, one plot is drawn. For the mlogit method, one plot for each selected random parameter is drawn.

Author(s)

Yves Croissant

See Also

mlogit() the estimation of random parameters logit models and rpar() for the description of rpar objects and distribution for functions which return informations about the distribution of random parameters.

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

lmtest

lrtest, waldtest

zoo

index

Risky Transportation Choices

Description

1793 choices by 561 individuals of a transport mode at Freetwon airport

Format

A dataframe containing:

• id: individual id,

• choice: 1 for the chosen mode,

• mode: one of Helicopter,WaterTaxi, ⁠Ferry, and ⁠Hovercraft',

• cost: the generalised cost of the transport mode,

• risk: the fatality rate, numbers of death per 100,000 trips,

• weight: weights,

• seats: ,

• noise: ,

• crowdness: ,

• convloc: ,

• clientele: ,

• chid: choice situation id,

• african: yes if born in Africa, no otherwise,

• lifeExp: declared life expectancy,

• dwage: declared hourly wage,

• iwage: imputed hourly wage,

• educ: level of education, one of low and high,

• fatalism: self-ranking of the degree of fatalism,

• gender: gender, one of female and male,

• age: age,

• haveChildren: yes if the traveler has children, no otherwise,

• swim: yes if the traveler knows how to swim, 'no, otherwise.

Source

American Economic Association data archive.

References

León G, Miguel E (2017). “Risky Transportation Choices and the Value of a Statistical Life.” American Economic Journal: Applied Economics, 9(1), 202-28. doi: 10.1257/app.20160140, https://www.aeaweb.org/articles?id=10.1257/app.20160140.

random parameter objects

Description

rpar objects contain the relevant information about estimated random parameters. The homonymous function extract on rpar object from a mlogit object.

Usage

rpar(x, par = NULL, norm = NULL, ...)

## S3 method for class 'rpar'
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

## S3 method for class 'rpar'
summary(object, ...)

Arguments

Details

mlogit objects contain an element called rpar which contain a list of rpar objects, one for each estimated random parameter. The print method prints the name of the distribution and the parameter, the summary behave like the one for numeric vectors.

Value

a rpar object, which contains:

• dist: the name of the distribution,

• mean: the first parameter of the distribution,

• sigma: the second parameter of the distribution,

• name: the name of the parameter.

Author(s)

Yves Croissant

See Also

mlogit() for the estimation of a random parameters logit model.

The three tests for mlogit models

Description

Three tests for mlogit models: specific methods for the Wald test and the likelihood ration test and a new function for the score test

Usage

scoretest(object, ...)

## S3 method for class 'mlogit'
scoretest(object, ...)

## Default S3 method:
scoretest(object, ...)

## S3 method for class 'mlogit'
waldtest(object, ...)

## S3 method for class 'mlogit'
lrtest(object, ...)

Arguments

Details

The scoretest function and mlogit method for waldtest and lrtest from the lmtest package provides the infrastructure to compute the three tests of hypothesis for mlogit objects.

The first argument must be a mlogit object. If the second one is a fitted model or a formula, the behaviour of the three functions is the one of the default methods of waldtest and lrtest: the two models provided should be nested and the hypothesis tested is that the constrained model is the ‘right’ model.

If no second model is provided and if the model provided is the constrained model, some specific arguments of mlogit should be provided to descibe how the initial model should be updated. If the first model is the unconstrained model, it is tested versus the ‘natural’ constrained model; for example, if the model is a heteroscedastic logit model, the constrained one is the multinomial logit model.

Value

an object of class htest.

Author(s)

Yves Croissant

Examples

library("mlogit")
library("lmtest")
data("TravelMode", package = "AER")
ml <- mlogit(choice ~ wait + travel + vcost, TravelMode,
             shape = "long", chid.var = "individual", alt.var = "mode")
hl <- mlogit(choice ~ wait + travel + vcost, TravelMode,
             shape = "long", chid.var = "individual", alt.var = "mode",
             method = "bfgs", heterosc = TRUE)
lrtest(ml, hl)
waldtest(hl)
scoretest(ml, heterosc = TRUE)

Stated Preferences for Train Traveling

Description

A sample of 235 Dutch individuals facing 2929 choice situations

Format

A dataframe containing:

• id: individual identifient,

• choiceid: choice identifient,

• choice: one of 'A' or 'B',

• price_z: price of proposition z (z = 'A', 'B') in cents of guilders,

• time_z: travel time of proposition z (z = 'A', 'B') in minutes,

• comfort_z: comfort of proposition z (z = 'A', 'B'), 0, 1 or 2 in decreasing comfort order,

• change_z: number of changes for proposition z (z = 'A', 'B').

Source

Journal of Applied Econometrics data archive.

References

Ben-Akiva M, Bolduc D, Bradley M (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.

Meijer E, Rouwendal J (2006). “Measuring welfare effects in models with random coefficients.” Journal of Applied Econometrics, 21(2), 227-244. doi: 10.1002/jae.841, https://onlinelibrary.wiley.com/doi/pdf/10.1002/jae.841, https://onlinelibrary.wiley.com/doi/abs/10.1002/jae.841.

vcov method for mlogit objects

Description

The vcov method for mlogit objects extract the covariance matrix of the coefficients, the errors or the random parameters.

Usage

## S3 method for class 'mlogit'
vcov(
  object,
  what = c("coefficient", "errors", "rpar"),
  subset = c("all", "iv", "sig", "sd", "sp", "chol"),
  type = c("cov", "cor", "sd"),
  reflevel = NULL,
  ...
)

## S3 method for class 'vcov.mlogit'
print(x, ...)

## S3 method for class 'vcov.mlogit'
summary(object, ...)

## S3 method for class 'summary.vcov.mlogit'
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

Arguments

Details

This new interface replaces the cor.mlogit and cov.mlogit functions which are deprecated.

Author(s)

Yves Croissant

See Also

mlogit() for the estimation of multinomial logit models.