library("stablelearner")Data-Ggnerating Function for Two-Class Problem
Description
Data-generating function to generate artificial data sets of a classification problem with two response classes, denoted as “A” and “B”.
Usage
dgp_twoclass(n = 100, p = 4, noise = 16, rho = 0,
b0 = 0, b = rep(1, p), fx = identity)
Arguments
n
|
integer. Number of observations. The default is 100. |
p
|
integer. Number of signal predictors. The default is 4. |
noise
|
integer. Number of noise predictors. The default is 16. |
rho
|
numeric value between -1 and 1 specifying the correlation between the signal predictors. The correlation is given by rho^k, where k is an integer value given by toeplitz structure. The default is 0 (no correlation between predictors).
|
b0
|
numeric value. Baseline probability for class “B” on the logit scale. The default is 0.
|
b
|
numeric value. Slope parameter for the predictors on the logit scale. The default is 1 for all predictors. |
fx
|
a function that is used to transform the predictors. The default is identity (equivalent to no transformation).
|
Value
A data.frame including a column denoted as class that is a factor with two levels “A” and “B”. All other columns represent the predictor variables (signal predictors followed by noise predictors) and are named by “x1”, “x2”, etc..
See Also
stability