## 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

 `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'

Max Kuhn

### Examples

```## in Chapter 11, 'Measuring Performance in Classification Model'
set.seed(975)

## 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

```

[Package AppliedPredictiveModeling version 1.1-7 Index]