Package 'mclogit'

Overdispersion in Multinomial Logit Models

Description

The function dispersion() extracts the dispersion parameter from a multinomial logit model or computes a dispersion parameter estimate based on a given method. This dispersion parameter can be attached to a model using update(). It can also given as an argument to summary().

Usage

dispersion(object,method, ...)
## S3 method for class 'mclogit'
dispersion(object,method=NULL,groups=NULL, ...)

Arguments

object	an object that inherits class "mclogit". When passed to dispersion(), it should be the result of a call of mclogit() of mblogit(), without random effects.
method	a character string, either "Afroz", "Fletcher", "Pearson", or "Deviance", that specifies the estimator of the dispersion; or NULL, in which case the default estimator, "Afroz" is used. The estimators are discussed in Afroz et al. (2019).
groups	an optional formula that specifies groups of observations relevant for the estimation of overdispersion. Prediced probabilities should be constant within groups, otherwise a warning is generated since the overdispersion estimate may be imprecise.
...	other arguments, ignored or passed to other methods.

References

Afroz, Farzana, Matt Parry, and David Fletcher. (2020). "Estimating Overdispersion in Sparse Multinomial Data." Biometrics 76(3): 834-842. doi:10.1111/biom.13194.

Examples

library(MASS) # For 'housing' data

# Note that with a factor response and frequency weighted data,
# Overdispersion will be overestimated:
house.mblogit <- mblogit(Sat ~ Infl + Type + Cont, weights = Freq,
                         data = housing)

dispersion(house.mblogit,method="Afroz")
dispersion(house.mblogit,method="Deviance")

summary(house.mblogit)

phi.Afroz <- dispersion(house.mblogit,method="Afroz")
summary(house.mblogit, dispersion=phi.Afroz)

summary(update(house.mblogit, dispersion="Afroz"))

# In order to be able to estimate overdispersion accurately,
# data like the above (which usually comes from applying
# 'as.data.frame' to a contingency table) the model has to be
# fitted with the optional argument 'from.table=TRUE':
house.mblogit.corrected <- mblogit(Sat ~ Infl + Type + Cont, weights = Freq,
                                   data = housing, from.table=TRUE,
                                   dispersion="Afroz")
# Now the estimated dispersion parameter is no longer larger than 20,
# but just bit over 1.0.
summary(house.mblogit.corrected)

---

Class, Party Position, and Electoral Choice

Description

This is an artificial data set on electoral choice as influenced by class and party positions.

Usage

data(electors)

Format

A data frame containing the following variables:

class

class position of voters

party

party that runs for election

Freq

freqency by which each party list is chosen by members of each class

time

time variable, runs from zero to one

econ.left

economic-policy "leftness" of each party

welfare

emphasis of welfare expansion of each party

auth

position on authoritarian issues

Examples

data(electors)

summary(mclogit(
  cbind(Freq,interaction(time,class))~econ.left+welfare+auth,
  data=electors))

summary(mclogit(
  cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
  data=electors))

## Not run: # This takes a bit longer.
summary(mclogit(
  cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
  random=~1|party.time,
  data=within(electors,party.time<-interaction(party,time))))

summary(mclogit(
  cbind(Freq,interaction(time,class))~econ.left/(class*time)+welfare/class+auth/class,
  random=~1|party.time,
  data=within(electors,{
        party.time <-interaction(party,time)
        econ.left.sq <- (econ.left-mean(econ.left))^2
        })))

## End(Not run)

---

'getSummary' Methods

Description

getSummary methods for use by mtable

Usage

## S3 method for class 'mblogit'
getSummary(obj,
            alpha=.05,
            ...)
## S3 method for class 'mclogit'
getSummary(obj,
            alpha=.05,
            rearrange=NULL,
            ...)
## S3 method for class 'mmblogit'
getSummary(obj,
            alpha=.05,
            ...)
## S3 method for class 'mmclogit'
getSummary(obj,
            alpha=.05,
            rearrange=NULL,
            ...)

Arguments

obj	an object returned by mblogit or mclogit
alpha	level of the confidence intervals; their coverage should be 1-alpha/2
rearrange	an optional named list of character vectors. Each element of the list designates a column in the table of estimates, and each element of a character vector refers to a coefficient. Names of list elements become column heads and names of the character vector elements become coefficient labels.
...	further arguments; ignored.

Examples

## Not run: 
summary(classd.model <- mclogit(cbind(Freq,choice.set)~
                   (econdim1.sq+nonmatdim1.sq+nonmatdim2.sq)+
                   (econdim1+nonmatdim1+nonmatdim2)+
                   (econdim1+nonmatdim1+nonmatdim2):classd,
                  data=mvoteint.classd,random=~1|mvoteint/eb,
                  subset=classd!="Farmers"))
myGetSummary.classd <- function(x)getSummary.mclogit(x,rearrange=list(
        "Econ. Left/Right"=c(
                    "Squared effect"="econdim1.sq",
                    "Linear effect"="econdim1",
                    " x Intermediate/Manual worker"="econdim1:classdIntermediate",
                    " x Service class/Manual worker"="econdim1:classdService class",
                    " x Self-employed/Manual worker"="econdim1:classdSelf-employed"
                    ),
        "Lib./Auth."=c(
                    "Squared effect"="nonmatdim1.sq",
                    "Linear effect"="nonmatdim1",
                    " x Intermediate/Manual worker"="nonmatdim1:classdIntermediate",
                    " x Service class/Manual worker"="nonmatdim1:classdService class",
                    " x Self-employed/Manual worker"="nonmatdim1:classdSelf-employed"
                    ),
        "Mod./Trad."=c(
                    "Squared effect"="nonmatdim2.sq",
                    "Linear effect"="nonmatdim2",
                    " x Intermediate/Manual worker"="nonmatdim2:classdIntermediate",
                    " x Service class/Manual worker"="nonmatdim2:classdService class",
                    " x Self-employed/Manual worker"="nonmatdim2:classdSelf-employed"
                    )
        ))

library(memisc)
mtable(classd.model,getSummary=myGetSummary.classd)
# Output would look like so:
# ==================================================================================
#                                 Econ. Left/Right    Lib./Auth.       Mod./Trad.
# ----------------------------------------------------------------------------------
# Squared effect                      0.030            0.008           -0.129**
#                                    (0.081)          (0.041)          (0.047)
# Linear effect                      -0.583***        -0.038            0.137**
#                                    (0.063)          (0.041)          (0.045)
#  x Intermediate/Manual worker       0.632***        -0.029           -0.015
#                                    (0.026)          (0.020)          (0.019)
#  x Service class/Manual worker      1.158***         0.084**          0.000
#                                    (0.040)          (0.032)          (0.030)
#  x Self-employed/Manual worker      1.140***         0.363***         0.112***
#                                    (0.035)          (0.027)          (0.026)
# Var(mvoteint)                       1.080***
#                                    (0.000)
# Var(mvoteint x eb)                  0.118***
#                                    (0.000)
# ----------------------------------------------------------------------------------
# Dispersion                              1.561
# Deviance                            15007.0
# N                                  173445
# ==================================================================================

## End(Not run)

---

Baseline-Category Logit Models for Categorical and Multinomial Responses

Description

The function mblogit fits baseline-category logit models for categorical and multinomial count responses with fixed alternatives.

Usage

mblogit(
  formula,
  data = parent.frame(),
  random = NULL,
  catCov = c("free", "diagonal", "single"),
  subset,
  weights = NULL,
  na.action = getOption("na.action"),
  model = TRUE,
  x = FALSE,
  y = TRUE,
  contrasts = NULL,
  method = NULL,
  estimator = c("ML", "REML"),
  dispersion = FALSE,
  start = NULL,
  from.table = FALSE,
  groups = NULL,
  control = if (length(random)) mmclogit.control(...) else mclogit.control(...),
  ...
)

Arguments

formula	the model formula. The response must be a factor or a matrix of counts.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which glm is called.
random	an optional formula or list of formulas that specify the random-effects structure or NULL.
catCov	a character string that specifies optional restrictions on the covariances of random effects between the logit equations. "free" means no restrictions, "diagonal" means that random effects pertinent to different categories are uncorrelated, while "single" means that the random effect variances pertinent to all categories are identical.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
weights	an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
model	a logical value indicating whether model frame should be included as a component of the returned value.
x, y	logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
method	NULL or a character string, either "PQL" or "MQL", specifies the type of the quasilikelihood approximation to be used if a random-effects model is to be estimated.
estimator	a character string; either "ML" or "REML", specifies which estimator is to be used/approximated.
dispersion	a logical value or a character string; whether and how a dispersion parameter should be estimated. For details see dispersion.
start	an optional matrix of starting values (with as many rows as logit equations). If the model has random effects, the matrix should have a "VarCov" attribute wtih starting values for the random effects (co-)variances. If the random effects model is estimated with the "PQL" method, the starting values matrix should also have a "random.effects" attribute, which should have the same structure as the "random.effects" component of an object returned by mblogit().
from.table	a logical value; do the data represent a contingency table, e.g. were created by applying as.data.frame() a the result of table() or xtabs(). This relevant only if the response is a factor. This argument should be set to TRUE if the data do come from a contingency table. Correctly setting from.table=TRUE in this case, will lead to efficiency gains in computing, but more importantly overdispersion will correctly be computed if present.
groups	an optional formula that specifies groups of observations relevant for the estimation of overdispersion. Covariates should be constant within groups, otherwise a warning is generated since the overdispersion estimate may be imprecise.
control	a list of parameters for the fitting process. See mclogit.control
...	arguments to be passed to mclogit.control or mmclogit.control

Details

The function mblogit internally rearranges the data into a 'long' format and uses mclogit.fit to compute estimates. Nevertheless, the 'user data' are unaffected.

Value

mblogit returns an object of class "mblogit", which has almost the same structure as an object of class "glm". The difference are the components coefficients, residuals, fitted.values, linear.predictors, and y, which are matrices with number of columns equal to the number of response categories minus one.

References

Agresti, Alan. 2002. Categorical Data Analysis. 2nd ed, Hoboken, NJ: Wiley. doi:10.1002/0471249688

Breslow, N.E. and D.G. Clayton. 1993. "Approximate Inference in Generalized Linear Mixed Models". Journal of the American Statistical Association 88 (421): 9-25. doi:10.1080/01621459.1993.10594284

See Also

The function multinom in package nnet also fits multinomial baseline-category logit models, but has a slightly less convenient output and does not support overdispersion or random effects. However, it provides some other options. Baseline-category logit models are also supported by the package VGAM, as well as some reduced-rank and (semi-parametric) additive generalisations. The package mnlogit estimates logit models in a way optimized for large numbers of alternatives.

Examples

library(MASS) # For 'housing' data
library(nnet)
library(memisc)

(house.mult<- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
                       data = housing))


(house.mblogit <- mblogit(Sat ~ Infl + Type + Cont, weights = Freq,
                         data = housing))

summary(house.mult)

summary(house.mblogit)

mtable(house.mblogit)

---

Conditional Logit Models and Mixed Conditional Logit Models

Description

mclogit fits conditional logit models and mixed conditional logit models to count data and individual choice data, where the choice set may vary across choice occasions.

Conditional logit models without random effects are fitted by Fisher-scoring/IWLS. Models with random effects (mixed conditional logit models) are estimated via maximum likelihood with a simple Laplace aproximation (aka PQL).

Usage

mclogit(formula, data=parent.frame(), random=NULL,
        subset, weights = NULL, offset=NULL, na.action = getOption("na.action"),
        model = TRUE, x = FALSE, y = TRUE, contrasts=NULL,
        method = NULL, estimator=c("ML","REML"),
        dispersion = FALSE,
        start=NULL,
        groups = NULL,
        control=if(length(random))
                    mmclogit.control(...)
                else mclogit.control(...), ...)

## S3 method for class 'mclogit'
update(object, formula., dispersion, ...)

## S3 method for class 'mclogit'
summary(object, dispersion = NULL, correlation = FALSE,
        symbolic.cor = FALSE,  ...)

Arguments

formula	a model formula: a symbolic description of the model to be fitted. The left-hand side should result in a two-column matrix. The first column contains the choice counts or choice indicators (alternative is chosen=1, is not chosen=0). The second column contains unique numbers for each choice set. The left-hand side can either take the form cbind(choice,set) or (from version 0.9.1) choice|set If individual-level data is used, choice sets correspond to individuals, if aggregated data with choice counts are used, choice sets usually correspond to covariate classes. The right-hand of the formula contains choice predictors. It should be noted that constants are deleted from the formula as are predictors that do not vary within choice sets.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which glm is called.
random	an optional formula or list of formulas that specify the random-effects structure or NULL.
weights	an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.
offset	an optional model offset. Currently only supported for models without random effects.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
start	an optional numerical vector of starting values for the conditional logit parameters. If the model has random effects, the vector should have a "VarCov" attribute wtih starting values for the random effects (co-)variances. If the random effects model is estimated with the "PQL" method, the starting values matrix should also have a "random.effects" attribute, which should have the same structure as the "random.effects" component of an object returned by mblogit().
model	a logical value indicating whether model frame should be included as a component of the returned value.
x, y	logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
method	NULL or a character string, either "PQL" or "MQL", specifies the type of the quasilikelihood approximation to be used if a random-effects model is to be estimated.
estimator	a character string; either "ML" or "REML", specifies which estimator is to be used/approximated.
dispersion	a real number used as dispersion parameter; a character vector that specifies the method to compute the dispersion; a logical value – if TRUE the default method ("Afroz") is used, if FALSE, the dispersion parameter is set to 1, that is, no dispersion. For details see dispersion.
groups	an optional formula that specifies groups of observations relevant for the estimation of overdispersion. Covariates should be constant within groups, otherwise a warning is generated since the overdispersion estimate may be imprecise.
control	a list of parameters for the fitting process. See mclogit.control
...	arguments to be passed to mclogit.control or mmclogit.control
object	an object that inherits class "mclogit". When passed to dispersion(), it should be the result of a call of mclogit() of mblogit(), without random effects.
formula.	a changes to the model formula, see update.default and update.formula.
correlation	logical; see summary.lm.
symbolic.cor	logical; see summary.lm.

Value

mclogit returns an object of class "mclogit", which has almost the same structure as an object of class "glm".

Note

Covariates that are constant within choice sets are automatically dropped from the model formula specified by the formula argument of mclogit.

If the model contains random effects, these should

• either vary within choice sets (e.g. the levels of a factor that defines the choice sets should not be nested within the levels of factor)

• or be random coefficients of covariates that vary within choice sets.

In earlier versions of the package (prior to 0.6) it will lead to a failure of the model fitting algorithm if these conditions are not satisfied. Since version 0.6 of the package, the function mclogit will complain about such model a misspecification explicitely.

From version 0.9.7 it is possible to choose the optimization technique used for the inner iterations of the PQL/MQL: either nlminb (the default), nlm, or any of the algorithms (other than "Brent" supported by optim). To choose the optimizer, use the appropriate argument for mmclogit.control .

References

Agresti, Alan (2002). Categorical Data Analysis. 2nd ed, Hoboken, NJ: Wiley. doi:10.1002/0471249688

Breslow, N.E. and D.G. Clayton (1993). "Approximate Inference in Generalized Linear Mixed Models". Journal of the American Statistical Association 88 (421): 9-25. doi:10.1080/01621459.1993.10594284

Elff, Martin (2009). "Social Divisions, Party Positions, and Electoral Behaviour". Electoral Studies 28(2): 297-308. doi:10.1016/j.electstud.2009.02.002

McFadden, D. (1973). "Conditionial Logit Analysis of Qualitative Choice Behavior". Pp. 105-135 in P. Zarembka (ed.). Frontiers in Econometrics. New York: Wiley. https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf

See Also

Conditional logit models are also supported by gmnl, mlogit, and survival. survival supports conditional logit models for binary panel data and case-control studies. mlogit and gmnl treat conditional logit models from an econometric perspective. Unlike the present package, they focus on the random utility interpretation of discrete choice models and support generalisations of conditional logit models, such as nested logit models, that are intended to overcome the IIA (indipendence from irrelevant alterantives) assumption. Mixed multinomial models are also supported and estimated using simulation-based techniques. Unlike the present package, mixed or random-effects extensions are mainly intended to fit repeated choices of the same individuals and not aggregated choices of many individuals facing identical alternatives.

Examples

data(Transport)

summary(mclogit(
  cbind(resp,suburb)~distance+cost,
  data=Transport
  ))
# New syntactic sugar:
summary(mclogit(
  resp|suburb~distance+cost,
  data=Transport
  ))


## Not run:  # This takes a bit longer.
data(electors)

electors <- within(electors,{
    party.time <-interaction(party,time)
    time.class <- interaction(time,class)
})

# Time points nested within parties
summary(mclogit(
  Freq|time.class~econ.left/class+welfare/class+auth/class,
  random=~1|party/time,
  data=electors))

# Party-level random intercepts and random slopes varying over time points
summary(mclogit(
  Freq|time.class~econ.left/class+welfare/class+auth/class,
  random=list(~1|party,~econ.left+0|time),
  data=electors))

## End(Not run)

---

Control Parameters for the Fitting Process

Description

mclogit.control returns a list of default parameters that control the fitting process of mclogit.

Usage

mclogit.control(epsilon = 1e-08,
                maxit = 25, trace=TRUE)
mmclogit.control(epsilon = 1e-08,
                 maxit = 25, trace=TRUE,
                 trace.inner=FALSE,
                 avoid.increase = FALSE,
                 break.on.increase = FALSE,
                 break.on.infinite = FALSE,
                 break.on.negative = FALSE,
                 inner.optimizer = "nlminb",
                 maxit.inner = switch(inner.optimizer,
                                      SANN          = 10000,
                                      `Nelder-Mead` = 500,
                                      100),
                 CG.type = 1,
                 NM.alpha = 1,
                 NM.beta = 0.5,
                 NM.gamma = 2.0,
                 SANN.temp = 10,
                 SANN.tmax = 10)

Arguments

epsilon	positive convergence tolerance $\epsilon$; the iterations converge when $|dev - dev_{old}|/(|dev| + 0.1) < \epsilon$.
maxit	integer giving the maximal number of IWLS or PQL iterations.
trace	logical indicating if output should be produced for each iteration.
trace.inner	logical; indicating if output should be produced for each inner iteration of the PQL method.
avoid.increase	logical; should an increase of the deviance be avoided by step truncation?
break.on.increase	logical; should an increase of the deviance be avoided by stopping the algorithm?
break.on.infinite	logical; should an infinite deviance stop the algorithm instead of leading to step truncation?
break.on.negative	logical; should a negative deviance stop the algorithm?
inner.optimizer	a character string, one of "nlminb", "nlm", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN". See nlminb, nlm, optim.
maxit.inner	integer; the maximum number of inner iterations
CG.type	integer; the type argument passed to optim if "CG" is selected as inner optimizer.
NM.alpha	integer; the alpha argument passed to optim if "Nelder-Mead" is selected as inner optimizer.
NM.beta	integer; the beta argument passed to optim if "Nelder-Mead" is selected as inner optimizer.
NM.gamma	integer; the gamma argument passed to optim if "Nelder-Mead" is selected as inner optimizer.
SANN.temp	integer; the temp argument passed to optim if "SANN" is selected as inner optimizer.
SANN.tmax	integer; the tmax argument passed to optim if "SANN" is selected as inner optimizer.

Value

A list.

---

Internal functions used for model fit.

Description

These functions are exported and documented for use by other packages. They are not intended for end users.

Usage

mclogit.fit(y, s, w, X,
            dispersion=FALSE,
            start = NULL, offset = NULL,
            control = mclogit.control())

mmclogit.fitPQLMQL(y, s, w, X, Z, d, 
                   start = NULL,
                   start.Phi = NULL,
                   start.b = NULL,
                   offset = NULL, method=c("PQL","MQL"),
                   estimator = c("ML","REML"),
                   control = mmclogit.control())

Arguments

y	a response vector. Should be binary.
s	a vector identifying individuals or covariate strata
w	a vector with observation weights.
X	a model matrix; required.
dispersion	a logical value or a character string; whether and how a dispersion parameter should be estimated. For details see dispersion.
Z	the random effects design matrix.
d	dimension of random effects. Typically $d=1$ for random intercepts only, $d>1$ for models with random intercepts.
start	an optional numerical vector of starting values for the coefficients.
offset	an optional model offset. Currently only supported for models without random effects.
start.Phi	an optional matrix of strarting values for the (co-)variance parameters.
start.b	an optional list of vectors with starting values for the random effects.
method	a character string, either "PQL" or "MQL", specifies the type of the quasilikelihood approximation.
estimator	a character string; either "ML" or "REML", specifies which estimator is to be used/approximated.
control	a list of parameters for the fitting process. See mclogit.control

Value

A list with components describing the fitted model.

---

Predicting responses or linear parts of the baseline-category and conditional logit models

Description

The predict() methods allow to obtain within-sample and out-of-sample predictions from models fitted with mclogit() and mblogit().

For models with random effecs fitted using the PQL-method, it is possible to obtain responses that are conditional on the reconstructed random effects.

Usage

## S3 method for class 'mblogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE, ...)
## S3 method for class 'mclogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE, ...)
## S3 method for class 'mmblogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE,
                             conditional=TRUE, ...)
## S3 method for class 'mmclogit'
predict(object, newdata=NULL,type=c("link","response"),se.fit=FALSE,
                             conditional=TRUE, ...)

Arguments

object	an object in class "mblogit", "mmblogit", "mclogit", or "mmclogit"
newdata	an optional data frame with new data
type	a character string specifying the kind of prediction
se.fit	a logical value; whether predictions should be accompanied with standard errors
conditional	a logical value; whether predictions should be made conditional on the random effects (or whether they are set to zero, i.e. their expectation). This argument is consequential only if the "mmblogit" or "mmclogit" object was created with method="PQL".
...	other arguments, ignored.

Value

The predict methods return either a matrix (unless called with se.fit=TRUE) or a list with two matrix-valued elements "fit" and "se.fit".

Examples

library(MASS)
(house.mblogit <- mblogit(Sat ~ Infl + Type + Cont, 
                          data = housing,
                          weights=Freq))

head(pred.house.mblogit <- predict(house.mblogit))
str(pred.house.mblogit <- predict(house.mblogit,se=TRUE))

head(pred.house.mblogit <- predict(house.mblogit,
                                   type="response"))
str(pred.house.mblogit <- predict(house.mblogit,se=TRUE,
                                  type="response"))
 # This takes a bit longer.
data(electors)
(mcre <- mclogit(
    cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
    random=~1|party.time,
    data=within(electors,party.time<-interaction(party,time))))

str(predict(mcre))
str(predict(mcre,type="response"))

str(predict(mcre,se.fit=TRUE))
str(predict(mcre,type="response",se.fit=TRUE))

---

Change baseline category of multinomial logit or similar model

Description

'rebase' returns an model object that is equivalent to the one given as argument but differs in parameterization

Usage

rebase(object, to, ...)

Arguments

object	a statistical model object
to	usually, a string; the baseline category

---

Simulating responses from baseline-category and conditional logit models

Description

The simulate() methods allow to simulate responses from models fitted with mclogit() and mblogit(). Currently only models without random effects are supported for this.

Usage

## S3 method for class 'mblogit'
simulate(object, nsim = 1, seed = NULL, ...)
## S3 method for class 'mclogit'
simulate(object, nsim = 1, seed = NULL, ...)

# These methods are currently just 'stubs', causing an error
# message stating that simulation from models with random
# effects are not supported yet
## S3 method for class 'mmblogit'
simulate(object, nsim = 1, seed = NULL, ...)
## S3 method for class 'mmclogit'
simulate(object, nsim = 1, seed = NULL, ...)

Arguments

object	an object from the relevant class
nsim	a number, specifying the number of simulated responses for each observation.
seed	an object specifying if and how the random number generator should be initialized ('seeded'). The interpetation of this argument follows the default method, see link[stats]{simulate}
...	other arguments, ignored.

Value

The result of the simulate method for objects created by mclogit is a data frame with one variable for each requested simulation run (their number is given by the nsim= argument). The contents of the columns are counts (or zero-one values), with group-wise multinomial distribution (within choice sets) just like it is assumed for the original response.

The shape of the result of the simulate method for objects created by mblogit is also a data frame. The variables within the data frame have a mode or shape that corresponds to the response to which the model was fitted. If the response is a matrix of counts, then the variables in the data frame are also matrices of counts. If the response is a factor and mblogit was called with an argument from.table=FALSE, the variables in the data frame are factors with the same factor levels as the response to which the model was fitted. If instead the function was called with from.table=TRUE, the variables in the data frame are counts, which represent frequency weights that would result from applying as.data.frame to a contingency table of simulated frequency counts.

Examples

library(MASS)
(house.mblogit <- mblogit(Sat ~ Infl + Type + Cont, 
                          data = housing,
                          weights=Freq,
                          from.table=TRUE))
sm <- simulate(house.mblogit,nsim=7)

housing.long <- housing[rep(seq.int(nrow(housing)),housing$Freq),]
(housel.mblogit <- mblogit(Sat ~ Infl + Type + Cont,
                           data=housing.long))
sml <- simulate(housel.mblogit,nsim=7)

housing.table <- xtabs(Freq~.,data=housing)
housing.mat <- memisc::to.data.frame(housing.table)
head(housing.mat)

(housem.mblogit <- mblogit(cbind(Low,Medium,High) ~
                               Infl + Type + Cont,
                           data=housing.mat))
smm <- simulate(housem.mblogit,nsim=7)

str(sm)
str(sml)
str(smm)

head(smm[[1]])

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Choice of Means of Transport

Description

This is an artificial data set on choice of means of transport based on cost and walking distance.

Usage

data(Transport)

Format

A data frame containing the following variables:

transport

means of transportation that can be chosen.

suburb

identifying number for each suburb

distance

walking distance to bus or train station

cost

cost of each means of transportation

working

size of working population of each suburb

prop.true

true choice probabilities

resp

choice frequencies of means of transportation

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