mixone: Generate data with independent and mixed-type predictors

In BartMixVs: Variable Selection Using Bayesian Additive Regression Trees

Generate data with independent and mixed-type predictors

Description

Generate data including responses and predictors values, of which predictors are independent and of mixed types.

Usage

mixone(n, p, sigma, binary)

Arguments

n	The number of observations.
p	The number of predictors.
sigma	The error variance.
binary	A boolean argument: binary = TRUE indicates that binary responses are generated and binary = FALSE indicates that continuous responses are generated.

Details

Sample the predictors x_1, ..., x_{ceiling(p/2)} from Bernoulli(0.5) independently and x_{ceiling(p/2)+1}, ..., x_p from Uniform(0, 1) independently. If binary = FALSE, sample the continuous response y from Normal(f0(x), σ^2), where

f0(x) = 10sin(π x_{ceiling(p/2)+1}*x_{ceiling(p/2)+2}) + 20(x_{ceiling(p/2)+3}-0.5)^2 + 10x_1 + 5x_2.

If binary = TRUE, sample the binary response y from Bernoulli(Φ(f0(x))) where f0 is defined above and Φ is the cumulative density function of the standard normal distribution.

Value

Return a list with the following components.

X	An n by p data frame representing predictors values, with each row corresponding an observation.
Y	A vector of length n representing response values.
f0	A vector of length n representing the values of f0(x).
sigma	The error variance which is only returned when binary = FALSE.
prob	A vector of length n representing the values of Φ(f0(x)), which is only returned when binary = TRUE.

Author(s)

Chuji Luo: [e-mail] and Michael J. Daniels: [e-mail].

References

Luo, C. and Daniels, M. J. (2021) "Variable Selection Using Bayesian Additive Regression Trees." arXiv preprint arXiv:2112.13998.

Examples

data = mixone(100, 10, 1, FALSE)

BartMixVs documentation built on May 5, 2022, 9:05 a.m.