ipred: Interfaces for ipred package for data science pipelines.

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

Description

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

Usage

ntbt_bagging(data, ...)
ntbt_errorest(data, ...)
ntbt_inbagg(data, ...)
ntbt_inclass(data, ...)
ntbt_slda(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(ipred)

## ntbt_bagging: Bagging Classification, Regression and Survival Trees
data("BreastCancer", package = "mlbench")

## Original function to interface
bagging(Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size
        + Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, data=BreastCancer, coob=TRUE)

## The interface puts data as first parameter
ntbt_bagging(BreastCancer, 
             Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size
             + Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, coob=TRUE)

## so it can be used easily in a pipeline.
BreastCancer %>%
  ntbt_bagging(Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + Epith.c.size
               + Bare.nuclei + Bl.cromatin + Normal.nucleoli + Mitoses, coob=TRUE)


## ntbt_errorest: Estimators of Prediction Error
data("iris")
library("MASS")
mypredict.lda <- function(object, newdata)
  predict(object, newdata = newdata)$class

## Original function to interface
errorest(Species ~ ., data = iris, model = lda, estimator = "cv", predict = mypredict.lda)

## The interface puts data as first parameter
ntbt_errorest(iris, Species ~ ., model = lda, estimator = "cv", predict = mypredict.lda)

## so it can be used easily in a pipeline.
iris %>%
  ntbt_errorest(Species ~ ., model = lda, estimator = "cv", predict = mypredict.lda)


## ntbt_inbagg: Indirect Bagging
library("MASS")
library("rpart")
y <- as.factor(sample(1:2, 100, replace = TRUE))
W <- mvrnorm(n = 200, mu = rep(0, 3), Sigma = diag(3))
X <- mvrnorm(n = 200, mu = rep(2, 3), Sigma = diag(3))
colnames(W) <- c("w1", "w2", "w3")
colnames(X) <- c("x1", "x2", "x3")
DATA <- data.frame(y, W, X)
pFUN <- list(list(formula = w1~x1+x2, model = lm, predict = mypredict.lm),
list(model = rpart))

## Original function to interface
inbagg(y ~ w1 + w2 + w3 ~ x1 + x2 + x3, data = DATA, pFUN = pFUN)

## The interface puts data as first parameter
ntbt_inbagg(DATA, y ~ w1 + w2 + w3 ~ x1 + x2 + x3, pFUN = pFUN)

## so it can be used easily in a pipeline.
DATA %>%
  ntbt_inbagg(y ~ w1 + w2 + w3 ~ x1 + x2 + x3, pFUN = pFUN)


## ntbt_inclass: Indirect Classification
data("Smoking", package = "ipred")
# Set three groups of variables:
# 1) explanatory variables are: TarY, NicY, COY, Sex, Age
# 2) intermediate variables are: TVPS, BPNL, COHB
# 3) response (resp) is defined by:
classify <- function(data) {
  data <- data[,c("TVPS", "BPNL", "COHB")]
  res <- t(t(data) > c(4438, 232.5, 58))
  res <- as.factor(ifelse(apply(res, 1, sum) > 2, 1, 0))
  res
}
response <- classify(Smoking[ ,c("TVPS", "BPNL", "COHB")])
smoking <- data.frame(Smoking, response)

## Original function to interface
inclass(response ~ TVPS + BPNL + COHB ~ TarY + NicY + COY + Sex + Age, data = smoking,
        pFUN = list(list(model = lm, predict = mypredict.lm)), cFUN = classify)

## The interface puts data as first parameter
ntbt_inclass(smoking, response ~ TVPS + BPNL + COHB ~ TarY + NicY + COY + Sex + Age,
             pFUN = list(list(model = lm, predict = mypredict.lm)), cFUN = classify)

## so it can be used easily in a pipeline.
smoking %>%
  ntbt_inclass(response ~ TVPS + BPNL + COHB ~ TarY + NicY + COY + Sex + Age,
               pFUN = list(list(model = lm, predict = mypredict.lm)), cFUN = classify)


## ntbt_slda: Stabilised Linear Discriminant Analysis
library("mlbench")
library("MASS")
learn <- as.data.frame(mlbench.twonorm(100))

## Original function to interface
slda(classes ~ ., data=learn)

## The interface puts data as first parameter
ntbt_slda(learn, classes ~ .)

## so it can be used easily in a pipeline.
learn %>%
  ntbt_slda(classes ~ .)

## End(Not run)

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