Some regression models for ordered categorical responses

```
Polr(formula, data, subset, weights, offset, cluster, na.action = na.omit,
method = c("logistic", "probit", "loglog", "cloglog"), ...)
```

formula

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.

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)`

.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

weights

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.

offset

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.

cluster

optional factor with a cluster ID employed for computing clustered covariances.

na.action

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.

method

a character describing the link function.

…

additional arguments to `tram`

.

An object of class `Polr`

, with corresponding `coef`

,
`vcov`

, `logLik`

, `estfun`

, `summary`

,
`print`

, `plot`

and `predict`

methods.

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, 10.1111/sjos.12291.

# NOT RUN { data("wine", package = "ordinal") library("MASS") polr(rating ~ temp + contact, data = wine) Polr(rating ~ temp + contact, data = wine) # }