no.process: Estimation for NPQR with No Inference
In quantreg.nonpar: Nonparametric Series Quantile Regression

Description Usage Arguments Value Author(s) References See Also

A method for the generic function npqr. It computes the quantile regression fits without performing inference

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no.process(data = data, taus, formula, basis = NULL, 
	var, load, rearrange=F, rearrange.vars="quantile", 
	average=T, nderivs=1, method = "fn")

`data`	a data.frame in which to interpret the variables named in the `formula` argument.
`taus`	a numerical vector, whose entries are strictly between 0 and 1, containing the quantile indexes of interest.
`formula`	a formula object, with the response Y on the left of a ~ operator, and the covariate terms, separated by + operators on the right, not including the regressor whose effect is to be estimated nonparametrically. Operators such as '*', ':', 'log()', and 'I()' are allowable. However, factor variables should be constructed prior to entry in the formula: the 'factor()' operator is not allowable.
`basis`	either a basis generated using the `fda` package of type "bspline" or "fourier", a factor variable, or an orthogonal polynomial basis generated using the `poly` command. This basis is the series regressor to be added to `formula`.
`var`	a column name within `data` whose values will be used, in combination with `basis`, to create the vectors used in the nonparametric part of the model.
`load`	optional manual input of loading vector (or matrix of loading vectors) that will be used as data points at which inference will be performed and over which hypothesis tests will be conducted. Each vector of `load` should be input as the concatenation of vectors whose entries correspond to the entries of v and Z(w), respectively (for example, the average values of each variable for the parametric part of the model, v, and a specific point for the nonparametric part of the model, Z(w)).
`rearrange`	a boolean specifiying whether estimates will be monotonized (requires that `average=FALSE` and `nderivs=0`).
`rearrange.vars`	if `rearrange = TRUE`, specifies whether monotonization will occur over "quantile", "var" (the variable of interest), or "both".
`average`	if `load` is not input, if `average=TRUE`, specifies that inference should be performed on the average value of a derivative (as specified by `nderivs`) of the conditional quantile function (inference cannot be performed when `average=TRUE` and `nderivs=0`). If `average=FALSE`, inference will be run at each unique value of the variable of interest in the dataset.
`nderivs`	the number of derivatives of the conditional quantile function upon which point estimates should be generated.
`method`	method to be implemented in quantile regressions: passed to function `rq`.

no.process returns a list containing the following elements:

`qfits`	a list whose length is equal to the length of `taus`. Each element is an `rq.object` returned by `rq` for the corresponding quantile.
`point.est`	a matrix containing the point estimates of interest (e.g., the average derivative of the function) for each pair of loading vectors and `taus`. The matrix is j by i, where j is the number of loading vectors specified (i.e., the number of observations in the dataset if `average=FALSE` and 1 if `average=TRUE`) and i is the number of `taus` specified.
`var.unique`	a vector containing all values of the covariate of interest with no repeated values.
`load`	the loading vector or matrix of loading vectors used as data points at which point estimates were generated. If `load` was not input by the user, `load` is generated based on `average` and `nderivs`.