mclogit (version 0.4.4)

mclogit: 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. The implementation of mixed conditional logit currently is limited to PQL and random intercepts.

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,
        start=NULL,start.theta=NULL,
        control=mclogit.control(…), …)

Arguments

formula

a model formula: a symbolic description of the model to be fitted. The left-hand side contains is expected to be 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.

If individual-level data is used, choice sets correspond to the individuals, if aggregated data with choice counts are used, choice sets may e.g. correspond to covariate classes within clusters.

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 that specifies 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.

start.theta

an optional numerical vector of starting values for the variance parameters.

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.

control

a list of parameters for the fitting process. See mclogit.control

…

arguments to be passed to mclogit.control

Value

mclogit returns an object of class "mclogit", 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.

Details

mclogit tries first to fit the model using the IRLS algorithm of glm.fit, which has the advantage that starting values are not needed in most cases. If convergence cannot achieved, it tries to minimize the deviance using optim with method "BFGS".

Examples

# NOT RUN {
data(Transport)

summary(mclogit(
  cbind(resp,suburb)~distance+cost,
  data=Transport
  ))

data(electors)

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))))
# }