# A data generation function using a discrete distribution for Rankin score rather than a normal distribution ```r generateDiscreteData(prevalence, N, support = 0L:6L, ctlDist, trtDist) ``` ## Arguments - `prevalence`: a vector of group prevalences (length denoted by J below) - `N`: the sample size to generate - `support`: the support values of the discrete distribution (length K), default 0:6 - `ctlDist`: a probability vector of length K denoting the Rankin score distribution for control. - `trtDist`: an K x J probability matrix with each column is the Rankin distribution for the associated group ## Returns a three-column data frame of `subGroup`, `trt` (0 or 1), and `score` ## Examples ```r # Simulate data from a discrete distribution for the Rankin scores, # which are typically ordinal integers from 0 to 6 in the following # simulations. So we define a few scenarios. library(ASSISTant) null.uniform <- rep(1, 7L) ## uniform on 7 support points hourglass <- c(1, 2, 2, 1, 2, 2, 1) inverted.hourglass <- c(2, 1, 1, 2, 1, 1, 2) bottom.heavy <- c(2, 2, 2, 1, 1, 1, 1) bottom.heavier <- c(3, 3, 2, 2, 1, 1, 1) top.heavy <- c(1, 1, 1, 1, 2, 2, 2) top.heavier <- c(1, 1, 1, 2, 2, 3, 3) ctlDist <- null.uniform trtDist <- cbind(null.uniform, null.uniform, hourglass, hourglass) ## 4 groups generateDiscreteData(prevalence = rep(1, 4), N = 10, ctlDist = ctlDist, trtDist = trtDist) ## default support is 0:6 trtDist <- cbind(bottom.heavy, bottom.heavy, top.heavy, top.heavy) generateDiscreteData(prevalence = rep(1, 4), N = 10, ctlDist = ctlDist, trtDist = trtDist) support <- c(-2, -1, 0, 1, 2) ## Support of distribution top.loaded <- c(1, 1, 1, 3, 3) ## Top is heavier ctl.dist <- c(1, 1, 1, 1, 1) ## null on 5 support points trt.dist <- cbind(ctl.dist, ctl.dist, top.loaded) ## 3 groups generateDiscreteData(prevalence = rep(1, 3), N = 10, support = support, ctlDist = ctl.dist, trtDist = trt.dist) ## ctl.dist can also be a matrix with different nulls for each subgroup uniform <- rep(1, 5) bot.loaded <- c(3, 3, 1, 1, 1) ctl.dist <- matrix(c(uniform, bot.loaded, top.loaded), nrow = 5) generateDiscreteData(prevalence = rep(1, 3), N = 10, support = support, ctlDist = ctl.dist, trtDist = trt.dist) ```

generateDiscreteData function