Once we have calculated the mean values and covariance matrices for all of the columns, we are ready to simulate a big dataset for any number of observations we desire.
For the covariance matrix, we can either use separate matrices for the two diabetes outcomes (1,0), or use a pooled covariance matrix, which shows the correlations among the variables regardless of the outcome.
We will use the separate correlation or covariance matrices since we have enough observations for each outcome (n=500 and n=268). If either of these classes were much smaller related to the other, we could use the pooled (or total) covariance matrix instead, since that would cover a larger set of observations.
Some notes on the code which follows:
- As a reminder, always start with a random seed prior to a simulation. That will ensure that you get the same random results every time you run the code.
- The
cor()function will compute the correlation matrix among all of the variables...