When I want to simulate Y coming from the linear regression model, $$Y_i = X_i ^T \beta + \epsilon_i,$$
I can use code like:
x = rnorm(100); beta = 1 y = x %*% beta + rnorm(100, sd = 3)
If I want to make the estimate of $\beta$ "noiser" (increase standard error), I can increase the standard deviation in the second line above.
How can I increase the standard error of a logistic regression coefficient? I can simulate from a logistic regression model with:
x = rnorm(100); beta = 1 nu = x %*% beta # linear predictor pr = 1/(1+exp(-nu_1)) # pass through an inv-logit function y = rbinom(100,1,pr) # bernoulli response variable