quantreg: Interfaces for quantreg package for data science pipelines.

In intubate: Interface to Popular R Functions for Data Science Pipelines

Description

Interfaces to quantreg functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_dynrq(data, ...)
ntbt_KhmaladzeTest(data, ...)
ntbt_nlrq(data, ...)
ntbt_rq(data, ...)
ntbt_rqProcess(data, ...)
ntbt_rqss(data, ...)

Arguments

data	data frame, tibble, list, ...
...	Other arguments passed to the corresponding interfaced function.

Details

Interfaces call their corresponding interfaced function.

Value

Object returned by interfaced function.

Author(s)

Roberto Bertolusso

Examples

## Not run: 
library(intubate)
library(magrittr)
library(quantreg)


## ntbt_dynrq: Dynamic Linear Quantile Regression
require(zoo)
data("UKDriverDeaths", package = "datasets")
dta <- data.frame(uk = log10(UKDriverDeaths))

## Original function to interface
dynrq(uk ~ L(uk, 1) + L(uk, 12), data = dta)

## The interface puts data as first parameter
ntbt_dynrq(dta, uk ~ L(uk, 1) + L(uk, 12))

## so it can be used easily in a pipeline.
dta %>%
  ntbt_dynrq(uk ~ L(uk, 1) + L(uk, 12))


## ntbt_KhmaladzeTest: Tests of Location and Location Scale Shift Hypotheses for Linear Models
data(barro)
## Original function to interface
KhmaladzeTest(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
              data = barro, taus = seq(.05,.95,by = .01))

## The interface puts data as first parameter
ntbt_KhmaladzeTest(barro, y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
                   taus = seq(.05,.95,by = .01))

## so it can be used easily in a pipeline.
barro %>%
  ntbt_KhmaladzeTest(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
                     taus = seq(.05,.95,by = .01))


## ntbt_nlrq: Function to compute nonlinear quantile regression estimates
Dat <- NULL; Dat$x <- rep(1:25, 20)
set.seed(1)
Dat$y <- SSlogis(Dat$x, 10, 12, 2)*rnorm(500, 1, 0.1)

## Original function to interface
nlrq(y ~ SSlogis(x, Asym, mid, scal), data = Dat, tau = 0.5, trace = TRUE)

## The interface puts data as first parameter
ntbt_nlrq(Dat, y ~ SSlogis(x, Asym, mid, scal), tau = 0.5, trace = TRUE)

## so it can be used easily in a pipeline.
Dat %>%
  ntbt_nlrq(y ~ SSlogis(x, Asym, mid, scal), tau = 0.5, trace = TRUE)


## ntbt_rq: Quantile Regression
data(stackloss)
dta <- data.frame(stack.loss, stack.x)

## Original function to interface
rq(stack.loss ~ stack.x, .5, data = dta)  # median (l1) regression  fit for the stackloss data. 

## The interface puts data as first parameter
ntbt_rq(dta, stack.loss ~ stack.x, .5)

## so it can be used easily in a pipeline.
dta %>%
  ntbt_rq(stack.loss ~ stack.x, .5)


## ntbt_rqProcess: Compute Standardized Quantile Regression Process
## Original function to interface
data(barro)
rqProcess(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
          data = barro, taus = seq(.05,.95,by = .01))

## The interface puts data as first parameter
ntbt_rqProcess(barro, y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
               taus = seq(.05,.95,by = .01))

## so it can be used easily in a pipeline.
barro %>%
  ntbt_rqProcess(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, 
                 taus = seq(.05,.95,by = .01))


## ntbt_rqss: Additive Quantile Regression Smoothing
n <- 200
x <- sort(rchisq(n,4))
z <- x + rnorm(n)
y <- log(x)+ .1*(log(x))^2 + log(x)*rnorm(n)/4 + z
dta <- data.frame(x, y, z)

## Original function to interface
rqss(y ~ qss(x, constraint= "N") + z, data = dta)

## The interface puts data as first parameter
ntbt_rqss(dta, y ~ qss(x, constraint= "N") + z)

## so it can be used easily in a pipeline.
dta %>%
  ntbt_rqss(y ~ qss(x, constraint= "N") + z)

## End(Not run)

intubate documentation built on May 2, 2019, 2:46 p.m.