These are objects of class "rq".
They represent the fit of a linear conditional quantile function model.
This class of objects is returned from the rq function
to represent a fitted linear quantile regression model.
The "rq" class of objects has methods for the following generic
functions:
coef, effects
, formula
, labels
, model.frame
, model.matrix
, plot
, logLik
, AIC
, extractAIC
, predict
, print
, print.summary
, residuals
, summary
The following components must be included in a legitimate rq object.
coefficientsthe coefficients of the quantile regression fit.
The names of the coefficients are the names of the
single-degree-of-freedom effects (the columns of the
model matrix).
If the model was fitted by method "br" with ci=TRUE, then
the coefficient component consists of a matrix whose
first column consists of the vector of estimated coefficients
and the second and third columns are the lower and upper
limits of a confidence interval for the respective coefficients.
residualsthe residuals from the fit.
dualthe vector dual variables from the fit.
rhoThe value(s) of objective function at the solution.
contrastsa list containing sufficient information to construct the contrasts used to fit any factors occurring in the model. The list contains entries that are either matrices or character vectors. When a factor is coded by contrasts, the corresponding contrast matrix is stored in this list. Factors that appear only as dummy variables and variables in the model that are matrices correspond to character vectors in the list. The character vector has the level names for a factor or the column labels for a matrix.
modeloptionally the model frame, if model=TRUE.
xoptionally the model matrix, if x=TRUE.
yoptionally the response, if y=TRUE.
The coefficients, residuals, and effects may be extracted
by the generic functions of the same name, rather than
by the $ operator. For pure rq objects this is less critical
than for some of the inheritor classes. In particular, for fitted rq objects
using "lasso" and "scad" penalties, logLik and AIC functions
compute degrees of freedom of the fitted model as the number of estimated
parameters whose absolute value exceeds a threshold edfThresh. By
default this threshold is 0.0001, but this can be passed via the AIC
function if this value is deemed unsatisfactory. The function AIC
is a generic function in R, with parameter k that controls the form
of the penalty: the default value of k is 2 which yields the classical
Akaike form of the penalty, while k <= 0 yields the Schwarz (BIC)
form of the penalty.
Note that the extractor function coef returns a vector with missing values
omitted.
rq, coefficients.