Usage
gbootstrap(data = data, B = B, taus, formula, basis = NULL, alpha = 0.05,
var, load, rearrange=F, rearrange.vars="quantile", uniform=F,
average=T, nderivs=1, method = "fn")
Arguments
data
a data.frame in which to interpret the variables named in the formula argument.
B
the number of bootstrap repetitions to be performed.
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.
alpha
a real number between 0 and 1: the desired significance level (e.g., 0.05).
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 prior to performing inference (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".
uniform
a boolean specifying whether inference will be uniform across observations and quantiles or done in a pointwise manner.
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 inference should be performed.
method
method to be implemented in quantile regressions: passed to function rq.