Polr {tram}

Ordered Categorical Regression

Description

Some regression models for ordered categorical responses

Usage

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

Arguments

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 NAs. 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.

Details

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).

Value

An object of class Polr, with corresponding coef, vcov, logLik, estfun, summary, print, plot and predict methods.

References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, doi:10.1111/sjos.12291.

Examples

  data("wine", package = "ordinal")

  library("MASS")
  polr(rating ~ temp + contact, data = wine)

  Polr(rating ~ temp + contact, data = wine)