R: Optional Fitting Method for Quantile Regression

rq.fit.conquer {quantreg} | R Documentation

## Optional Fitting Method for Quantile Regression

### Description

This fitting method provides a link to the gradient descent for convolution smoothed quantile regression problem implemented in the conquer package of He et al (2020).

### Usage

    rq.fit.conquer (x, y, tau=0.5, kernel = c("Gaussian", "uniform",
        "parabolic", "triangular"), h = 0, tol = 1e-04,
        iteMax = 5000, ci = FALSE, alpha = 0.05, B = 200)

### Arguments

`x` | design matrix usually supplied via rq(), expected to have a intercept as the first column
`y` | response vector usually supplied via rq()
`tau` | quantile of interest
`kernel` | A character string specifying the choice of kernel function. Default is "Gaussian". Other choices are "uniform", "parabolic" or "triangular".
`h` | The bandwidth parameter for kernel smoothing of the QR objective function. Default is max((log(n) + p) / n)^0.4, 0.05. The default is used if the input value is less than 0.05.
`tol` | Tolerance level of the gradient descent algorithm. The gradient descent algorithm terminates when the maximal entry of the gradient is less than "tol". Default is 1e-05.
`iteMax` | Maximum number of iterations. Default is 5000.
`ci` | A logical flag. Default is FALSE. If "ci = TRUE", then three types of confidence intervals (percentile, pivotal and normal) will be constructed via multiplier bootstrap. This option is subsumed in normal use by the `summary.rq` functionality.
`alpha` | Nominal level for confidence intervals, may be passed via the call to `summary`
`B` | Number of bootstrap replications. May be passed via summary.

### Details

See documentation in the conquer package.

### Value

Returns an object of class "rq".

### References

Xuming He and Xiaoou Pan and Kean Ming Tan and Wen-Xin Zhou, (2020) conquer: Convolution-Type Smoothed Quantile Regression, https://CRAN.R-project.org/package=conquer

### See Also

`rq`

[Package quantreg version 5.83 Index]