MASS: Interfaces for MASS package for data science pipelines.

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

Description

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

Usage

ntbt_corresp(data, ...)
ntbt_glm.nb(data, ...)
ntbt_lda(data, ...)
ntbt_lm.gls(data, ...)
ntbt_lm.ridge(data, ...)
ntbt_loglm(data, ...)
ntbt_logtrans(data, ...)
ntbt_polr(data, ...)
ntbt_qda(data, ...)
ntbt_rlm(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(MASS)

## corresp
## Original function to interface
corresp(~ Age + Eth, data = quine)

## The interface reverses the order of data and formula
ntbt_corresp(data = quine, ~ Age + Eth)

## so it can be used easily in a pipeline.
quine %>%
  ntbt_corresp(~ Age + Eth)
  
## glm.nb
## Original function to interface
glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)

## The interface reverses the order of data and formula
ntbt_glm.nb(data = quine, Days ~ Sex/(Age + Eth*Lrn))

## so it can be used easily in a pipeline.
quine %>%
  ntbt_glm.nb(Days ~ Sex/(Age + Eth*Lrn))

## lda
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
                   Sp = rep(c("s","c","v"), rep(50,3)))

## Original function to interface
lda(Sp ~ ., Iris)

## The interface reverses the order of data and formula
ntbt_lda(Iris, Sp ~ .)

## so it can be used easily in a pipeline.
Iris %>%
  ntbt_lda(Sp ~ .)

stackloss %>%
  ntbt_lda(stack.loss ~ .) %>%
  summary()

## lm.gls
## Original function to interface
lm.gls(conc ~ uptake, CO2, W = diag(nrow(CO2)))

## The interface reverses the order of data and formula
ntbt_lm.gls(CO2, conc ~ uptake, W = diag(nrow(CO2)))

## so it can be used easily in a pipeline.
CO2 %>%
  ntbt_lm.gls(conc ~ uptake, W = diag(nrow(CO2)))

## lm.ridge
## Original function to interface
lm.ridge(GNP.deflator ~ ., longley)

## The interface reverses the order of data and formula
ntbt_lm.ridge(longley, GNP.deflator ~ .)

## so it can be used easily in a pipeline.
longley %>%
  ntbt_lm.ridge(GNP.deflator ~ .)

## loglm
## Original function to interface
xtCars93 <- xtabs(~ Type + Origin, Cars93)
loglm(~ Type + Origin, xtCars93)

## The interface reverses the order of data and formula
xtCars93 <- ntbt_xtabs(Cars93, ~ Type + Origin)
ntbt_loglm(xtCars93, ~ Type + Origin)

## so it can be used easily in a pipeline.
Cars93 %>%
  ntbt_xtabs(~ Type + Origin) %>%
  ntbt_loglm(~ Type + Origin)

## logtrans
## Original function to interface
logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
         alpha = seq(0.75, 6.5, len=20))

## The interface reverses the order of data and formula
ntbt_logtrans(data = quine, Days ~ Age*Sex*Eth*Lrn,
              alpha = seq(0.75, 6.5, len=20))

## so it can be used easily in a pipeline.
quine %>%
  ntbt_logtrans(Days ~ Age*Sex*Eth*Lrn,
                alpha = seq(0.75, 6.5, len=20))

## polr
op <- options(contrasts = c("contr.treatment", "contr.poly"))

## Original function to interface
polr(Sat ~ Infl + Type + Cont, housing)

## The interface reverses the order of data and formula
ntbt_polr(housing, Sat ~ Infl + Type + Cont)

## so it can be used easily in a pipeline.
housing %>%
  ntbt_polr(Sat ~ Infl + Type + Cont)

options(op)

## qda
set.seed(123) ## make reproducible
tr <- sample(1:50, 25)

iris3df <- data.frame(cl = factor(c(rep("s",25), rep("c",25), rep("v",25))),
                      train = rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]))

## Original function to interface
qda(cl ~ ., iris3df)

## The interface reverses the order of data and formula
ntbt_qda(iris3df, cl ~ .)

## so it can be used easily in a pipeline.
iris3df %>%
  ntbt_qda(cl ~ .)

## rlm
## Original function to interface
rlm(stack.loss ~ ., stackloss)

## The interface reverses the order of data and formula
ntbt_rlm(stackloss, stack.loss ~ .)

## so it can be used easily in a pipeline.
stackloss %>%
  ntbt_rlm(stack.loss ~ .) %>%
  summary()
  
stackloss %>%
  ntbt_rlm(stack.loss ~ ., psi = psi.hampel, init = "lts") %>%
  summary()

stackloss %>%
  ntbt_rlm(stack.loss ~ ., psi = psi.bisquare) %>%
  summary()

## End(Not run)

rbertolusso/intubate documentation built on May 27, 2019, 3 a.m.