SimData: Simulate data with normally distributed predictors and binary...
In stepPenal: Stepwise Forward Variable Selection in Penalized Regression

Description Usage Arguments Details Value Examples

Simulate data with normally distributed predictors and binary response

1	SimData(N, beta, noise, corr = TRUE, corr.effect = 0.5)

`N`	sample size
`beta`	coefficients (effect of informative predictors)
`noise`	variables (effect of uninformative predictors)
`corr`	Logical, if FALSE the function generates uncorrelated predictors, if TRUE the correlation between predictors is 0.5 by default and the user can supply a different value in the corr.effect argument.
`corr.effect`	the correlation between informative predictors.

The response y follows a Binomial distribution with probability= exp(X*beta)/(1+exp(X*beta))

A data frame N x p, where p is the total number of informative and uninformative predictors. The first column of the dataframe is the binary response variable y

# simulate data with N=100 (sample size) and 23 predictors; 4 informative and 20 noise

set.seed(14)
beta    <- c(3, 2, -1.6, -4)
noise <- 5
N     <- 100
simData <- SimData(N=N, beta=beta, noise=noise, corr=FALSE)