Linear Quantile Regression Object
These are objects of class
They represent the fit of a linear conditional quantile function model.
The coefficients, residuals, and effects may be extracted
by the generic functions of the same name, rather than
$ 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,
compute degrees of freedom of the fitted model as the number of estimated
parameters whose absolute value exceeds a threshold
default this threshold is 0.0001, but this can be passed via the
function if this value is deemed unsatisfactory. The function
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
This class of objects is returned from the
to represent a fitted linear quantile regression model.
"rq" class of objects has methods for the following generic
The following components must be included in a legitimate
the 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
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.
the residuals from the fit.
the vector dual variables from the fit.
The value(s) of objective function at the solution.
a 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.
optionally the model frame, if
optionally the model matrix, if
optionally the response, if