quadBoundaryFunc {AppliedPredictiveModeling} | R Documentation |
These functions simulate data that are used in the text.
quadBoundaryFunc(n) easyBoundaryFunc(n, intercept = 0, interaction = 2)
n |
the sample size |
intercept |
the coefficient for the logistic regression intercept term |
interaction |
the coefficient for the logistic regression interaction term |
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.
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
## 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