st: simulating data for logistic regression: translating R to Stata

[Michael McCulloch <mm@pinest.org>]

Hello,
I'm simulating data for logistic regression (code for variable 
generation is below) , and am missing the last step, linking exposure 
to disease. Disease will be a y/n variable. I want to translate R 
code to Stata, which specifies a population model where

  # Specify population model for log odds that Y=1
    L <- .1*(comorb-2) + .045*(age-50) +
      (log(cholesterol - 10)-5.2)*(-2*(treat=='a') +
          3.5*(treat=='b')+2*(treat=='c'))
    # Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)]
    y <- ifelse(runif(n) < plogis(L), 1, 0)
the log odds that Y=1 is given by:

Might someone suggest how I can write that equation in Stata?
Below is Stata code for variable generation, to be used by that above equation.

clear
set obs 1000				//create 1000 observations
gen id=_n				//Create ID numbers
set seed 12358				//set random number seed for 
reproducibility
gen treat= 1 + int(3*uniform())	//Generate treatment: discrete 
uniform random variables, 1 2 or 3
gen comorb= 1 + int(4*uniform())	//Generate treatment: 
discrete uniform random variables, 1,2,3,4
gen age=50+10 * invnorm(uniform())
gen chol = 200+25 * invnorm(uniform())	// create cholesterol 
variable, with mean 200 and sd==25
gen wt = 150+20 * invnorm(uniform())	// create cholesterol 
variable, with mean 200 and sd==25
gen sex= 1 + int(2*uniform())	//Generate sex: discrete uniform 
random variables, 1 or 2
replace sex=0 if sex==2
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