Functions for Simulating Data

Description

These functions simulate data that are used in the text.

Usage

quadBoundaryFunc(n)

easyBoundaryFunc(n, intercept = 0, interaction = 2)

Arguments

Details

The quadBoundaryFunc function creates a class boundary that is a function of both predictors. The probability values are based on a logistic regression model with model equation: -1-2*X1 -0.2*X1^2 + 2*X2^2. The predictors here are multivariate normal with mean (1, 0) and a moderate degree of positive correlation.

Similarly, the easyBoundaryFunc uses a logistic regression model with model equation: intercept -4*X1 + 4*X2 + interaction*X1*X2. The predictors here are multivariate normal with mean (1, 0) and a strong positive correlation.

Value

Both functions return data frames with columns

Author(s)

Max Kuhn

Examples

## in Chapter 11, 'Measuring Performance in Classification Model'
set.seed(975)
training <- quadBoundaryFunc(500)
testing <- quadBoundaryFunc(1000)
 

## in Chapter 20, 'Factors That Can Affect Model Performance'
set.seed(615)
dat <- easyBoundaryFunc(200, interaction = 3, intercept = 3)
dat$X1 <- scale(dat$X1)
dat$X2 <- scale(dat$X2)
dat$Data <- "Original"
dat$prob <- NULL

## in Chapter X, 'An Introduction to Feature Selection'

set.seed(874)
reliefEx3 <- easyBoundaryFunc(500)
reliefEx3$X1 <- scale(reliefEx3$X1)
reliefEx3$X2 <- scale(reliefEx3$X2)
reliefEx3$prob <- NULL

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AppliedPredictiveModeling)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AppliedPredictiveModeling/quadBoundaryFunc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: quadBoundaryFunc
> ### Title: Functions for Simulating Data
> ### Aliases: quadBoundaryFunc easyBoundaryFunc
> ### Keywords: utilities
> 
> ### ** Examples
> 
> ## in Chapter 11, 'Measuring Performance in Classification Model'
> set.seed(975)
> training <- quadBoundaryFunc(500)
> testing <- quadBoundaryFunc(1000)
>  
> 
> ## in Chapter 20, 'Factors That Can Affect Model Performance'
> set.seed(615)
> dat <- easyBoundaryFunc(200, interaction = 3, intercept = 3)
> dat$X1 <- scale(dat$X1)
> dat$X2 <- scale(dat$X2)
> dat$Data <- "Original"
> dat$prob <- NULL
> 
> ## in Chapter X, 'An Introduction to Feature Selection'
> 
> set.seed(874)
> reliefEx3 <- easyBoundaryFunc(500)
> reliefEx3$X1 <- scale(reliefEx3$X1)
> reliefEx3$X2 <- scale(reliefEx3$X2)
> reliefEx3$prob <- NULL
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>