Simulate dataset

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.

Value

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

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

Examples

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