partykit: Interfaces for partykit package for data science pipelines.

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

Description

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

Usage

# Commented functions already defined for package party
# ntbt_cforest(data, ...)
# ntbt_ctree(data, ...)
ntbt_glmtree(data, ...)
ntbt_lmtree(data, ...)
# ntbt_mob(data, ...)
ntbt_palmtree(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(partykit)


## ntbt_cforest: Conditional Random Forests

## Original function to interface
cf <- cforest(dist ~ speed, data = cars)

## The interface puts data as first parameter
cf <- ntbt_cforest(cars, dist ~ speed)

## so it can be used easily in a pipeline.
cf <- cars %>%
  ntbt_cforest(dist ~ speed)


## ntbt_ctree: Conditional Inference Trees
airq <- subset(airquality, !is.na(Ozone))

## Original function to interface
airct <- ctree(Ozone ~ ., data = airq)
plot(airct)

## The interface puts data as first parameter
airct <- ntbt_ctree(airq, Ozone ~ .)
plot(airct)

## so it can be used easily in a pipeline.
airq %>%
  ntbt_ctree(Ozone ~ .) %>%
  plot()


## ntbt_glmtree: Generalized Linear Model Trees
data("PimaIndiansDiabetes", package = "mlbench")

## Original function to interface
pid_tree2 <- glmtree(diabetes ~ glucose | pregnant +
                       pressure + triceps + insulin + mass + pedigree + age,
                     data = PimaIndiansDiabetes, family = binomial)
plot(pid_tree2)

## The interface puts data as first parameter
pid_tree2 <- ntbt_glmtree(PimaIndiansDiabetes, diabetes ~ glucose | pregnant +
                            pressure + triceps + insulin + mass + pedigree + age,
                          family = binomial)
plot(pid_tree2)

## so it can be used easily in a pipeline.
PimaIndiansDiabetes %>%
  ntbt_glmtree(diabetes ~ glucose | pregnant +
                 pressure + triceps + insulin + mass + pedigree + age,
               family = binomial) %>%
  plot()


## ntbt_lmtree: Linear Model Trees
data("BostonHousing", package = "mlbench")
BostonHousing <- 
  transform(BostonHousing,
            chas = factor(chas, levels = 0:1, labels = c("no", "yes")),
            rad = factor(rad, ordered = TRUE))

## Original function to interface
bh_tree <- lmtree(medv ~ log(lstat) + I(rm^2) | zn + indus + chas +
                    nox + age + dis + rad + tax + crim + b + ptratio,
                  data = BostonHousing, minsize = 40)
plot(bh_tree)

## The interface puts data as first parameter
bh_tree <- ntbt_lmtree(BostonHousing,
                       medv ~ log(lstat) + I(rm^2) | zn + indus + chas +
                         nox + age + dis + rad + tax + crim + b + ptratio,
                       minsize = 40)
plot(bh_tree)

## so it can be used easily in a pipeline.
BostonHousing %>%
  ntbt_lmtree(medv ~ log(lstat) + I(rm^2) | zn + indus + chas +
                nox + age + dis + rad + tax + crim + b + ptratio,
              minsize = 40) %>%
  plot()


## ntbt_mob: Model-based Recursive Partitioning
data("PimaIndiansDiabetes", package = "mlbench")

logit <- function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  glm(y ~ 0 + x, family = binomial, start = start, ...)
}

## Original function to interface
pid_tree <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin +
                  mass + pedigree + age, data = PimaIndiansDiabetes, fit = logit)
plot(pid_tree)

## The interface puts data as first parameter
pid_tree <- ntbt_mob(PimaIndiansDiabetes, diabetes ~ glucose | pregnant + pressure +
                       triceps + insulin + mass + pedigree + age, fit = logit)
plot(pid_tree)

## so it can be used easily in a pipeline.
PimaIndiansDiabetes %>%
  ntbt_mob(diabetes ~ glucose | pregnant + pressure +
             triceps + insulin + mass + pedigree + age, fit = logit) %>%
  plot()


## ntbt_palmtree: Partially Additive (Generalized) Linear Model Trees
dgp <- function(nobs = 1000, nreg = 5, creg = 0.4, ptreat = 0.5, sd = 1,
  coef = c(1, 0.25, 0.25, 0, 0, -0.25), eff = 1)
{
  d <- mvtnorm::rmvnorm(nobs,
    mean = rep(0, nreg),
    sigma = diag(1 - creg, nreg) + creg)
  colnames(d) <- paste0("x", 1:nreg)
  d <- as.data.frame(d)
  d$a <- rbinom(nobs, size = 1, prob = ptreat)
  d$err <- rnorm(nobs, mean = 0, sd = sd)

  gopt <- function(d) {
    as.numeric(d$x1 > -0.545) * as.numeric(d$x2 < 0.545)
  }
  d$y <- coef[1] + drop(as.matrix(d[, paste0("x", 1:5)]) %*% coef[-1]) -
    eff * (d$a - gopt(d))^2 + d$err
  d$a <- factor(d$a)
  return(d)
}
set.seed(1)
d <- dgp()

## Original function to interface
palm <- palmtree(y ~ a | x1 + x2 + x5 | x1 + x2 + x3 + x4 + x5, data = d)
plot(palm)

## The interface puts data as first parameter
palm <- ntbt_palmtree(d, y ~ a | x1 + x2 + x5 | x1 + x2 + x3 + x4 + x5)
plot(palm)

## so it can be used easily in a pipeline.
d %>%
  ntbt_palmtree(y ~ a | x1 + x2 + x5 | x1 + x2 + x3 + x4 + x5) %>%
  plot()

## End(Not run)

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