Simulating the negative cases

We just simulated the positive cases. Now, let's set up some similar code to simulate the non-diabetes patients (outcome=0).

For the negative cases, we will also multiply sample.bin by -1, so that in the future, we know that all the positive sample.bin instances correspond to positive cases and all the negative sample.bin instances correspond to negative ones:

set.seed(123) 
 
nbins2=base::round(n2/400,0) 
correlationMatrix <- cor(PimaIndians[PimaIndians$diabetes =='neg',1:8]) 
 
covarianceMatrix <- stats::cov(PimaIndians[PimaIndians$diabetes =='neg',1:8]) 
 
out_sd2 <- as.DataFrame(data.frame(data.frame( 
 sample.bin=base::sample(1:nbins2,n2,replace=TRUE)*(-1),  
 outcome=0, 
 mvrnorm(n2, mu = means.neg, Sigma = matrix(covarianceMatrix, ncol = 8),  empirical = TRUE) 
))[rep(1:n2, times=2000), ]) 
 
nrow(out_sd2)  

The output...