dgp_twoclass: Data-Ggnerating Function for Two-Class Problem
In stablelearner: Stability Assessment of Statistical Learning Methods

dgp_twoclass

R Documentation

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..