linodds: An object for modeling linear odds.

In shabbychef/ohenery: Modeling of Ordinal Random Variables via Softmax Regression

An object for modeling linear odds.

Description

A model for odds linear in some feature.

Usage

as.linodds(object, formula, beta)

## S3 method for class 'linodds'
predict(object, newdata, type = c("eta", "mu",
  "erank"), na.action = na.pass, group = NULL, ...)

## S3 method for class 'linodds'
coef(object, ...)

Arguments

object	some list-like object.
formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.
beta	the fit coefficients.
newdata	a data.frame from which we can extract a model frame via the formula of the object.
type	indicates which prediction should be returned: eta The odds. mu The probability. erank The expected rank.
na.action	How to deal with missing values in y, g, X, wt, eta0.
group	the string name of the group variable in the data, or a bare character with the group name. The group indices need not be integers, but that is more efficient. They need not be sorted.
...	other arguments.

Details

An object which holds a formula, some fit coefficients \beta which fit in that formula to generate odds in odds space. The odds can then be converted, via predict.linodds to probabilities, or to expected ranks under the Harville model. Both harsm and hensm return objects of class linodds.

We think of linear odds as \eta = x^{\top}\beta, for independent variables x. The odds, \eta are converted to probabilities, \mu via \mu = c \exp{\eta}, where the constant c is chosen so the \mu for a given matching sum to one.

Author(s)

Steven E. Pav [e-mail]

See Also

harsm, hensm.

smax, harsm_invlink.

shabbychef/ohenery documentation built on Oct. 19, 2023, 12:08 p.m.