Usage
mlogit(formula, data, subset, weights, na.action,
alt.subset = NULL, reflevel = NULL, ...)
## S3 method for class 'mlogit':
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
## S3 method for class 'mlogit':
summary(object, ...)
## S3 method for class 'summary.mlogit':
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
## 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':
vcov(object, ...)
## S3 method for class 'mlogit':
residuals(object, outcome = TRUE, ...)
## S3 method for class 'mlogit':
fitted(object, outcome = TRUE, ...)
## S3 method for class 'mlogit':
df.residual(object, ...)
## S3 method for class 'mlogit':
terms(x, ...)
## S3 method for class 'mlogit':
estfun(x, ...)
## S3 method for class 'mlogit':
bread(x, ...)
## S3 method for class 'mlogit':
model.matrix(object, ...)
## S3 method for class 'mlogit':
update(object, new, ...)Arguments
x, object
an object of class mlogit
formula
a symbolic description of the model to be estimated,
new
an updated formula for the update method,
subset
an optional vector specifying a subset of observations,
weights
an optional vector of weights,
na.action
a function which indicates what should happen when
the data contains 'NA's,
alt.subset
a vector of character strings containing the subset of
alternative on which the model should be estimated,
reflevel
the base alternative (the one for which the
coefficients of individual-specific variables are normalized to 0),
digits
the number of digits,
width
the width of the printing,
outcome
a boolean which indicates, for the fitted and the
residuals methods whether a matrix (for each choice, one value
for each alternative) or a vector (for each choice, only a value for
the alternative chosen) should be returne