quadBoundaryFunc: Functions for Simulating Data

In AppliedPredictiveModeling: Functions and Data Sets for 'Applied Predictive Modeling'

Description

These functions simulate data that are used in the text.

Usage

quadBoundaryFunc(n)

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

Arguments

n	the sample size
intercept	the coefficient for the logistic regression intercept term
interaction	the coefficient for the logistic regression interaction term

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

X1	numeric predictor value
X2	numeric predictor value
prob	numeric value reflecting the true probability of the first class
class	a factor variable with levels 'Class1' and 'Class2'

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

AppliedPredictiveModeling documentation built on May 2, 2019, 6:13 p.m.