# polr

##### Ordered Logistic or Probit Regression

Fits a logistic or probit regression model to an ordered factor
response. The default logistic case is *proportional odds
logistic regression*, after which the function is named.

- Keywords
- models

##### Usage

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

##### 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. - 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. This is in the format
`c(coefficients, zeta)`

: see the Values section. - ...
- 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. 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 or cauchit (corresponding to a Cauchy latent variable).

##### Details

This model is what Agresti (2002) calls a *cumulative link*
model. The basic interpretation is as a *coarsened* version of a
latent variable $Y_i$ which has a logistic or normal or
extreme-value or Cauchy distribution with scale parameter one and a
linear model for the mean. The ordered factor which is observed is
which bin $Y_i$ falls into with breakpoints
$$\zeta_0 = -\infty < \zeta_1 < \cdots < \zeta_K = \infty$$
This leads to the model
$$\mbox{logit} P(Y \le k | x) = \zeta_k - \eta$$
with *logit* replaced by *probit* for a normal latent
variable, and $\eta$ being the linear predictor, a linear
function of the explanatory variables (with no intercept). Note
that it is quite common for other software to use the opposite sign
for $\eta$ (and hence the coefficients `beta`

).

In the logistic case, the left-hand side of the last display is the
log odds of category $k$ or less, and since these are log odds
which differ only by a constant for different $k$, the odds are
proportional. Hence the term *proportional odds logistic
regression*.

In the complementary log-log case, we have a *proportional
hazards* model for grouped survival times.

There are methods for the standard model-fitting functions, including
`predict`

, `summary`

, `vcov`

,
`anova`

, `model.frame`

and an
`extractAIC`

method for use with `stepAIC`

. There
are also `profile`

and `confint`

methods.

##### 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).model (if `model`

is true).

##### References

Agresti, A. (2002) *Categorical Data.* Second edition. Wiley.
Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S.* Fourth edition. Springer.

##### See Also

##### Examples

```
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
house.plr
summary(house.plr, digits = 3)
## slightly worse fit from
summary(update(house.plr, method = "probit", Hess = TRUE), digits = 3)
## although it is not really appropriate, can fit
summary(update(house.plr, method = "cloglog", Hess = TRUE), digits = 3)
predict(house.plr, housing, type = "p")
addterm(house.plr, ~.^2, test = "Chisq")
house.plr2 <- stepAIC(house.plr, ~.^2)
house.plr2$anova
anova(house.plr, house.plr2)
house.plr <- update(house.plr, Hess=TRUE)
pr <- profile(house.plr)
confint(pr)
plot(pr)
pairs(pr)
```

*Documentation reproduced from package MASS, version 7.3-14, License: GPL-2 | GPL-3*