# dgp_twoclass

##### Data-Ggnerating Function for Two-Class Problem

Data-generating function to generate artificial data sets of a classification
problem with two response classes, denoted as `"A"`

and `"B"`

.

- Keywords
- resampling, similarity

##### 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

##### Examples

```
# NOT RUN {
dgp_twoclass(n = 200, p = 6, noise = 4)
# }
```

*Documentation reproduced from package stablelearner, version 0.1-2, License: GPL-2 | GPL-3*