newpolr {PResiduals}

slightly modified version of polr from MASS

Description

slightly modified version of polr from MASS

Usage

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

Arguments

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 NAs. 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).

Value

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).

References

polr from MASS

See Also

optim, glm, multinom