miscmethods.mlogit: Methods for mlogit objects

Description

Miscellaneous methods for `mlogit` objects.

Usage

# S3 method for mlogit
residuals(object, outcome = TRUE, ...)
# S3 method for mlogit
df.residual(object, ...)
# S3 method for mlogit
terms(x, ...)
# S3 method for mlogit
model.matrix(object, ...)
model.response.mlogit(object, ...)
# S3 method for mlogit
update(object, new, ...)
# S3 method for mlogit
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)
# S3 method for mlogit
logLik(object, ...)
# S3 method for mlogit
summary(object, ..., type = c("chol", "cov", "cor"))
# S3 method for summary.mlogit
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)
# S3 method for mlogit
idx(x, n = NULL, m = NULL)
# S3 method for mlogit
idx_name(x, n = NULL, m = NULL)
# S3 method for mlogit
predict(object, newdata = NULL, returnData = FALSE, ...)
# S3 method for mlogit
fitted(
  object,
  type = c("outcome", "probabilities", "linpred", "parameters"),
  outcome = NULL,
  ...
)
# S3 method for mlogit
coef(
  object,
  subset = c("all", "iv", "sig", "sd", "sp", "chol"),
  fixed = FALSE,
  ...
)
# S3 method for summary.mlogit
coef(object, ...)

Arguments

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 returned,
...: further arguments.
x, object: an object of class `mlogit`
new: an updated formula for the `update` method,
digits: the number of digits,
width: the width of the printing,
type: one of `outcome` (probability of the chosen alternative), `probabilities` (probabilities for all the alternatives), `parameters` for individual-level random parameters for the fitted method, how the correlated random parameters should be displayed : `"chol"` for the estimated parameters (the elements of the Cholesky decomposition matrix), `"cov"` for the covariance matrix and `"cor"` for the correlation matrix and the standard deviations,
n, m: see [dfidx::idx()]
newdata: a `data.frame` for the `predict` method,
returnData: for the `predict` method, if `TRUE`, the data is returned as an attribute,
subset: an optional vector of coefficients to extract for the `coef` method,
fixed: if `FALSE` (the default), constant coefficients are not returned,