SimData {bkmr} | R Documentation |
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 |
family |
a description of the error distribution and link function to be used in the model. Currently implemented for |
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()