
Some regression models for ordered categorical responses
Polr(formula, data, subset, weights, offset, cluster, na.action = na.omit,
method = c("logistic", "probit", "loglog", "cloglog", "cauchit"), ...)
An object of class Polr
, with corresponding coef
,
vcov
, logLik
, estfun
, summary
,
print
, plot
and predict
methods.
an object of class "formula"
: a symbolic description
of the model structure to be
fitted. The details of model specification are given under
tram
and in the package vignette.
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)
.
an optional vector specifying a subset of observations to be used in the fitting process.
an optional vector of weights to be used in the fitting
process. Should be NULL
or a numeric vector. If present,
the weighted log-likelihood is maximised.
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be NULL
or a numeric vector of length equal to the
number of cases.
optional factor with a cluster ID employed for computing clustered covariances.
a function which indicates what should happen when the data
contain NA
s. The default is set by the na.action
setting
of options
, and is na.fail
if that is unset.
a character describing the link function.
additional arguments to tram
.
Models for ordered categorical responses reusing the interface of
polr
. Allows for stratification, censoring and
trunction.
The model is defined with a negative shift term, thus exp(coef())
is the multiplicative change of the odds ratio (conditional odds for
reference divided by conditional odds of treatment or for a one unit
increase in a numeric variable). Large values of the
linear predictor correspond to large values of the conditional
expectation response (but this relationship is nonlinear).
Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110--134, tools:::Rd_expr_doi("10.1111/sjos.12291").
data("wine", package = "ordinal")
library("MASS")
polr(rating ~ temp + contact, data = wine)
Polr(rating ~ temp + contact, data = wine)
Run the code above in your browser using DataLab