bkmr: SimData – R documentation

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SimData

Simulate dataset

Simulate predictor, covariate, and continuous outcome data

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

`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.

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

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

Bayesian Kernel Machine Regression

v0.2.0

GPL-2

Authors

Jennifer F. Bobb [aut, cre]

Initial release