intubate (version 1.0.0)

quantreg: Interfaces for quantreg package 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.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

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)