DGP: Data generating process used by Battey, H. S. & Cox, D. R....

In HCmodelSets: Regression with a Large Number of Potential Explanatory Variables

Data generating process used by Battey, H. S. & Cox, D. R. (2018).

Description

This function generates realizations of random variables as described in the simple example of Battey, H. S. & Cox, D. R. (2018).

Usage

DGP(s,a,sigStrength,rho,n,noise=NULL,var,d,intercept,type.response="N",DGP.seed=NULL,
    scale=NULL,shape=NULL,rate=NULL)

Arguments

s	Number of signal variables.
a	Number of noise variables correlated with signal variables.
sigStrength	Signal strength.
rho	Correlation among signal variables and noise variables correlated with signal variables.
n	Sample size.
noise	Variance of the observations around the true regression line.
var	Variance of the potential explanatory variables.
d	Number of potential explanatory variables.
intercept	Expected value of the response variable when all potential explanatory variables are at zero. It is only considered when type.response="N".
type.response	Generates gaussian ("N") or survival ("S") data from a proportional hazards model with Weibull baseline hazard.
DGP.seed	Seed for the random number generator.
scale	scale parameter of the proportional hazards model with Weibull baseline hazard.
shape	shape parameter of the proportional hazards model with Weibull baseline hazard.
rate	rate parameter of the exponential distribution of censoring times. If not provided, uncensored data are generated.

Value

X	The simulated design matrix.
Y	The simulated response variable.
TRUE.idx	Indices of the variables in the true model.
status	If type.response="S", provides the status from survival data.

Acknowledgement

The work was supported by the UK Engineering and Physical Sciences Research Council under grant number EP/P002757/1.

Author(s)

Hoeltgebaum, H. H.

References

Cox, D. R. and Battey, H. S. (2017). Large numbers of explanatory variables, a semi-descriptive analysis. Proceedings of the National Academy of Sciences, 114(32), 8592-8595.

Battey, H. S. and Cox, D. R. (2018). Large numbers of explanatory variables: a probabilistic assessment. Proceedings of the Royal Society of London, A., 474(2215), 20170631.

Hoeltgebaum, H., & Battey, H. S. (2019). HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions. The R Journal, 11(2), 370-379.

Examples

## Generates DGP

## Generates a random DGP
dgp = DGP(s=5, a=3, sigStrength=1, rho=0.9, n=100, intercept=5, noise=1,
          var=1, d=1000, DGP.seed = 2018)
          

HCmodelSets documentation built on March 31, 2023, 7:02 p.m.