Returns an object of class
"rq.process" that represents a quantile regression fit.
rq(formula, tau=.5, data, weights, na.action, method="br", model = TRUE, contrasts, ...)
- a formula object, with the response on the left of a
~operator, and the terms, separated by
+operators, on the right.
- the quantile(s) to be estimated, this is generally a number between 0 and 1,
but if specified outside this range, it is presumed that the solutions
for all values of
tauin (0,1) are desired. In the former case an object of cla
- a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument. If this is missing, then the variables in the formula should be on the search list. This may also be a single number to handl
- vector of observation weights; if supplied, the algorithm fits to minimize the sum of the weights multiplied into the absolute residuals. The length of weights must be the same as the number of observations. The weights must be nonnegative
- a function to filter missing data.
This is applied to the model.frame after any subset argument has been used.
The default (with
na.fail) is to create an error if any missing values are found. A possible alternative is
- if TRUE then the model frame is returned. This is essential if one wants to call summary subsequently.
- the algorithmic method used to compute the fit. There are currently
four options: The default method is the modified version of the
Barrodale and Roberts algorithm for $l_1$-regression,
l1fitin S, and is described in
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