# rqProcess

##### Compute Standardized Quantile Regression Process

Computes a standardize quantile regression process for the model
specified by the formula, on the partition of [0,1] specified by the
taus argument, and standardized according to the argument nullH.
Intended for use in `KhmaladzeTest`

.

- Keywords
- regression

##### Usage

`rqProcess(formula, data, taus, nullH = "location", ...)`

##### Arguments

- formula
model formula

- data
data frame to be used to interpret formula

- taus
quantiles at which the process is to be evaluated, if any of the taus lie outside (0,1) then the full process is computed for all distinct solutions.

- nullH
Null hypothesis to be used for standardization

- ...
optional arguments passed to

`summary.rq`

##### Details

The process computes standardized estimates based on the
hypothesis specified in the `nullH`

argument.
The Vhat component is rescaled by the Cholesky
decomposition of the tau specific covariance matrix, the vhat component is
rescaled by the marginal standard errors. The nature of the covariance
matrix used for the standardization is controlled arguments passed via
the `...`

argument to `summary.rq`

. If the full
process is estimated then these covariance options aren't available
and only a simple iid-error form of the covariance matrix is used.

##### Value

The points of evaluation of the process

Values of xbar'betahat(taus)

Joint parametric QR process

Marginal parametric QR processes

##### See Also

*Documentation reproduced from package quantreg, version 5.54, License: GPL (>= 2)*