Simulate Gaussian response from a sparse regression model

Simulate Gaussian response from a sparse regression model

generate_y(X, p_nn, a)

Arguments

X

data.frame with numeric and factor columns only.

p_nn

number of non-null covariate predictors. The regression coefficients (beta) corresponding to columns 1:p_nn of x will be non-zero, all other are set to zero.

a

amplitude of non-null regression coefficients

Value

simulated Gaussian response from regression model y = x x is the (scaled) model.matrix of X.

Details

This function takes as input data.frame X (created with the function generate_X) that may consist of both numeric and binary factor columns. This data frame is then expanded to a model matrix x (with the model.matrix function) and subsequently scaled in the same way as LASSO scaling. Next we simulate y ~ N(x the remaining coefficients (p_nn+1):ncol(x) are set to zero.

See also

Other generate: generate_Weib(), generate_X(), generate_lp()

Examples

set.seed(1)
# Simulate 4 Gaussian and 2 binary covariate predictors:
X <- generate_X(n=100, p=6, p_b=2, cov_type="cov_equi", rho=0.5)
# Simulate response from model y = 2*X[,1] + 2*X[,2] + epsilon, where epsilon ~ N(0,1)
y <- generate_y(X, p_nn=2, a=2)