# 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..

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