test.R

In roywilsonLinc/gbmFns: Functions to enhance gbm analyses and reporting

install_github("roywilsonLinc/gbmFns")

library(gbmFns)


install.packages("roxygen2")

library(devtools)
library(roxygen2)

library(gbmFns)

devtools::document()
devtools::load_all()

# 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)
# fit initial model
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)

library(gbm)
library(ggplot2)
library(cowplot)
library(reshape2)

?gbmFns