Simulate Conditional Longitudinal Ordinal Data with Logistic Specification This is a conditional model with a random intercept

TODO: Build in check if the correlations are too high! Where is that code? Note that the variances aren't used if passed, Requires MASS R package

sim_dat_ord_logistic_conditional(
  N = 500,
  number.groups = 2,
  number.timepoints = 4,
  reg.formula = NULL,
  Beta = 0,
  thresholds = c(-2, -1, 0, 1),
  subject.var = 1,
  cond.mcar = F,
  Covariate = F
)

Arguments

reg.formula	this is a regression formula to pass to the data generation; null is formula(~ Group + Time + Time*Group),
Beta	this is the Beta for the regression equation; numeric matrix of Beta values OR a scalar value != 0 that will be the final value of the interaction parameters, default is all(Beta == 0) for Type I error simulations
thresholds	the ordinal generation uses a logistic approach, and these are the tresholds
subject.var	the subject-level variance, also known as the variance associated with the random intercept
cond.mcar	logical; do you want a conditional MCAR data generation
Covariate	logical; do you want a random simulated covariate?

Value

returns a dataframe containing the simulated data