| quadBoundaryFunc {AppliedPredictiveModeling} | R Documentation |
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: -1-2X_1 -0.2X_1^2 + 2X_2^2. 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 -4X_1 + 4X_2 + interaction \times X_1 \times X_2. 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