regDataGen {CORElearn} R Documentation

## Artificial data for testing regression algorithms

### Description

The generator produces regression data data with 4 discrete and 7 numeric attributes.

### Usage

```  regDataGen(noInst, t1=0.8, t2=0.5, noise=0.1)
```

### Arguments

 `noInst` Number of instances to generate. `t1, t2` Parameters controlling the shape of the distribution. `noise` Parameter controlling the amount of noise. If `noise=0`, there is no noise. If noise = 1, then the level of the signal and noise are the same.

### Details

The response variable is derived from x4, x5, x6 using two different functions. The choice depends on a hidden variable, which determines weather the response value would follow a linear dependency f=x_4-2x_5+3x_6, or a nonlinear one f=cos(4π x_4)(2x_5-3x_6).

Attributes a1, a2, x1, x2 carry some information on the hidden variables depending on parameters t1, t2. Extreme values of the parameters are t1=0.5 and t2=1, when there is no information. On the other hand, if t1=0 or t1=1 then each of the attributes a1, a2 carries full information. If t2=0, then each of x1, x2 carries full information on the hidden variable.

The attributes x4, x5, x6 are available with a noise level depending on parameter `noise`. If `noise=0`, there is no noise. If `noise=1`, then the level of the signal and noise are the same.

### Value

Returns a `data.frame` with `noInst` rows and 11 columns. Range of values of the attributes and response are

 `a1` 0,1 `a2` a,b,c,d `a3` 0,1 (irrelevant) `a4` a,b,c,d (irrelevant) `x1` numeric (gaussian with different sd for each class) `x2` numeric (gaussian with different sd for each class) `x3` numeric (gaussian, irrelevant) `x4` numeric from [0,1] `x5` numeric from [0,1] `x6` numeric from [0,1] `response` numeric

### Author(s)

Petr Savicky

`classDataGen`,`ordDataGen`,`CoreModel`,

### Examples

```#prepare a regression data set
regData <-regDataGen(noInst=200)

# build regression tree similar to CART
modelRT <- CoreModel(response ~ ., regData, model="regTree", modelTypeReg=1)
print(modelRT)

destroyModels(modelRT) # clean up

```

[Package CORElearn version 1.56.0 Index]