stablelearner (version 0.1-4)

dgp_twoclass: 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)

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..

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).

See Also

stability

Examples

dgp_twoclass(n = 200, p = 6, noise = 4)