| 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 |
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(4\pi 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
See Also
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