polr {MASS} | R Documentation |
Fits a proportional odd logistic regression model to an ordered factor response.
polr(formula, data=NULL, weights, start, ..., subset, na.action=na.fail, contrasts=NULL, Hess=FALSE)
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. |
start |
initial values for the parameters. |
... |
additional arguments (currently ignored). |
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. |
"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 message returned by nlminb .
|
niter |
the number of iterations, function and gradient evaluations used by nlminb .
|
Hessian |
(if Hess is true).
|
options(contrasts=c("contr.treatment", "contr.poly")) data(housing) 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