R: Conditional Logit Models and Mixed Conditional Logit Models
mclogit
R Documentation
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, offset=NULL, na.action = getOption("na.action"),
model = TRUE, x = FALSE, y = TRUE, contrasts=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.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
Details
mlogit 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".
Value
mlogit returns an object of class "mlogit", 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.