Simulation of quantitative laboratory variables using multivariate Gaussian approach given mean, standard deviation, and correlation coefficient.
- load the 'real world' data and calculate mean and sd
- calculate correlation matrix for all selected laboratory variables
- create a covariance matrix and simulate data based on mean, sd, and the covariance matrix
- put the mask of missing from the realworld dataframe on simulated dataframe
- comparing the distribution and correlation of real world and simulated data with the same mask
R code deposit at "05022022_simulation_gnesis.R" simulated data with mask on deposit at "dffinal_repeat_select_widformat_t0_masked_test_1st_version2.RData" in version 2.