newpolr: slightly modified version of polr from MASS
In PResiduals: Probability-Scale Residuals and Residual Correlations

Description Usage Arguments Value References See Also

slightly modified version of polr from MASS

newpolr(
  formula,
  data,
  weights,
  start,
  ...,
  subset,
  na.action,
  contrasts = NULL,
  Hess = FALSE,
  model = TRUE,
  method = c("logit", "probit", "cloglog", "loglog", "cauchit")
)

`formula`	a formula
`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)`, typically the environment from which `cobot` is called.
`weights`	optional case weights in fitting. Default to 1.
`start`	initial values for the parameters.
`...`	additional arguments to be passed to `optim`, most often a `control` argument.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`na.action`	a function which indicates what should happen when the data contain `NA`s. The default is is `na.fail`. Another possible value is `NULL`, no action. Value `na.exclude` can be useful.
`contrasts`	a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
`Hess`	logical for whether the Hessian (the observed information matrix) should be returned. Use this if you intend to call `summary` or `vcov` on the fit.
`model`	logical for whether the model matrix should be returned.
`method`	logistic or probit or complementary log-log, loglog, or cauchit (corresponding to a Cauchy latent variable).

A object of class "polr". This has components

`coefficients`	the coefficients of the linear predictor, which has no intercept.
`zeta`	the intercepts for the class boundaries.
`deviance`	the residual deviance.
`fitted.values`	a matrix, with a column for each level of the response.
`lev`	the names of the response levels.
`terms`	the `terms` structure describing the model.
`df.residual`	the number of residual degrees of freedoms, calculated using the weights.
`edf`	the (effective) number of degrees of freedom used by the model
`n, nobs`	the (effective) number of observations, calculated using the weights. (`nobs` is for use by `stepAIC`).
`call`	the matched call.
`method`	the matched method used.
`convergence`	the convergence code returned by `optim`.
`niter`	the number of function and gradient evaluations used by `optim`.
`lp`	the linear predictor (including any offset).
`Hessian`	(if `Hess` is true). Note that this is a numerical approximation derived from the optimization proces.
`model`	(if `model` is true).