Description Usage Arguments Details Value Author(s) Examples
Interfaces to quantreg
functions that can be used
in a pipeline implemented by magrittr
.
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data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
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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)
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