no.process {quantreg.nonpar}

Estimation for NPQR with No Inference

Description

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

Usage

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

Arguments

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.

Value

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.

Author(s)

Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov, Ivan Fernandez-Val

References

Belloni, A., Chernozhukov, V., and I. Fernandez-Val (2011), "Conditional quantile processes based on series or many regressors," arXiv:1105.6154.

See Also

npqr