SimData: Simulate dataset

In jenfb/bkmr: Bayesian Kernel Machine Regression

Simulate dataset

Description

Simulate predictor, covariate, and continuous outcome data

Usage

SimData(
  n = 100,
  M = 5,
  sigsq.true = 0.5,
  beta.true = 2,
  hfun = 3,
  Zgen = "norm",
  ind = 1:2,
  family = "gaussian"
)

Arguments

n	Number of observations
M	Number of predictor variables to generate
sigsq.true	Variance of normally distributed residual error
beta.true	Coefficient on the covariate
hfun	An integer from 1 to 3 identifying which predictor-response function to generate
Zgen	Method for generating the matrix Z of exposure variables, taking one of the values c("unif", "norm", "corr", "realistic")
ind	select which predictor(s) will be included in the h function; how many predictors that can be included will depend on which h function is being used.
family	a description of the error distribution and link function to be used in the model. Currently implemented for gaussian and binomial families.

Details

• hfun = 1: A nonlinear function of the first predictor

• hfun = 2: A linear function of the first two predictors and their product term

• hfun = 3: A nonlinear and nonadditive function of the first two predictor variables

Value

a list containing the parameter values and generated variables of the simulated datasets

Examples

set.seed(5)
dat <- SimData()

jenfb/bkmr documentation built on Nov. 17, 2024, 4:46 a.m.