gbm: Interfaces for gbm package for data science pipelines.

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

Description

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

Arguments

data	data frame, tibble, list (or object coercible by as.data.frame to a data frame) containing the variables in the model.
...	Other arguments passed to gbm.

Details

ntbt_gbm calls gbm.

Value

Object returned by gbm function.

Author(s)

Roberto Bertolusso

Examples

## Not run: 
library(intubate)
library(magrittr)
library(gbm)

# A least squares regression example # create some data
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
X4 <- factor(sample(letters[1:6],N,replace=TRUE))
X5 <- factor(sample(letters[1:3],N,replace=TRUE))
X6 <- 3*runif(N) 
mu <- c(-1,0,1,2)[as.numeric(X3)]

SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)

# introduce some missing values
X1[sample(1:N,size=500)] <- NA
X4[sample(1:N,size=300)] <- NA

data <- data.frame(Y = Y, X1 = X1, X2 = X2, X3 = X3,
                   X4 = X4, X5 = X5, X6 = X6)

## Original function to interface
gbm1 <-
  gbm(Y~X1+X2+X3+X4+X5+X6,         # formula
      data=data,                   # dataset
      var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,
      # +1: monotone increase,
      #  0: no monotone restrictions
      distribution="gaussian",     # see the help for other choices
      n.trees=1000,                # number of trees
      shrinkage=0.05,              # shrinkage or learning rate,
      # 0.001 to 0.1 usually work
      interaction.depth=3,         # 1: additive model, 2: two-way interactions, etc.
      bag.fraction = 0.5,          # subsampling fraction, 0.5 is probably best
      train.fraction = 0.5,        # fraction of data for training,
      # first train.fraction*N used for training
      n.minobsinnode = 10,         # minimum total weight needed in each node
      cv.folds = 3,                # do 3-fold cross-validation
      keep.data=TRUE,              # keep a copy of the dataset with the object
      verbose=FALSE,               # don't print out progress
      n.cores=1)                   # use only a single core (detecting #cores is
# error-prone, so avoided here)
summary(gbm1)

## The interface reverses the order of data and formula
gbm1 <-
  ntbt_gbm(data=data,                   # dataset
           Y~X1+X2+X3+X4+X5+X6,         # formula
           var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,
           # +1: monotone increase,
           #  0: no monotone restrictions
           distribution="gaussian",     # see the help for other choices
           n.trees=1000,                # number of trees
           shrinkage=0.05,              # shrinkage or learning rate,
           # 0.001 to 0.1 usually work
           interaction.depth=3,         # 1: additive model, 2: two-way interactions, etc.
           bag.fraction = 0.5,          # subsampling fraction, 0.5 is probably best
           train.fraction = 0.5,        # fraction of data for training,
           # first train.fraction*N used for training
           n.minobsinnode = 10,         # minimum total weight needed in each node
           cv.folds = 3,                # do 3-fold cross-validation
           keep.data=TRUE,              # keep a copy of the dataset with the object
           verbose=FALSE,               # don't print out progress
           n.cores=1)                   # use only a single core (detecting #cores is
# error-prone, so avoided here)

## so it can be used easily in a pipeline.
data %>%
  ntbt_gbm(Y~X1+X2+X3+X4+X5+X6,         # formula
           var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,
           # +1: monotone increase,
           #  0: no monotone restrictions
           distribution="gaussian",     # see the help for other choices
           n.trees=1000,                # number of trees
           shrinkage=0.05,              # shrinkage or learning rate,
           # 0.001 to 0.1 usually work
           interaction.depth=3,         # 1: additive model, 2: two-way interactions, etc.
           bag.fraction = 0.5,          # subsampling fraction, 0.5 is probably best
           train.fraction = 0.5,        # fraction of data for training,
           # first train.fraction*N used for training
           n.minobsinnode = 10,         # minimum total weight needed in each node
           cv.folds = 3,                # do 3-fold cross-validation
           keep.data=TRUE,              # keep a copy of the dataset with the object
           verbose=FALSE,               # don't print out progress
           n.cores=1) %>%
  summary()

## End(Not run)

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