polr {MASS} | R Documentation

## Proportional Odds Logistic Regression

### Description

Fits a proportional odd logistic regression model to an ordered factor response.

### Usage

    polr(formula, data, weights, start, ..., subset, na.action,
         contrasts = NULL, Hess = FALSE, model = TRUE)

### Arguments

`formula`

a formula expression as for regression models, of the form `response ~ predictors`. The response should be a factor (preferably an ordered factor), which will be interpreted as an ordinal response, with levels ordered as in the factor. A proportional odds model will be fitted. The model must have an intercept: attempts to remove one will lead to a warning and be ignored. An offset may be used. See the documentation of `formula` for other details.

`data` | an optional data frame in which to interpret the variables occurring in `formula`.
`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` | expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
`na.action` | a function to filter missing data.
`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.
`model` | logical for whether the model matrix should be returned.

### Value

A object of class `"polr"`.

`coefficients` | the coefficients of the linear predictor.
`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` | the (effective) number of observations, calculated using the weights
`call` | the matched call.
`convergence` | the convergence code returned by `optim`.
`niter` | the number of function and gradient evaluations used by `optim`.
`Hessian` | (if `Hess` is true).
`model` | (if `model` is true).

### References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

### See Also

`optim`, `glm`, `multinom`.

### Examples

    options(contrasts = c("contr.treatment", "contr.poly"))
    house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
    house.plr
    summary(house.plr)
    predict(house.plr, housing, type = "p")
    addterm(house.plr, ~.^2, test = "Chisq")
    house.plr2 <- stepAIC(house.plr, ~.^2)
    house.plr2$anova

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