set.seed(4) N <- 2000 Operators <- sample(5:15, N, replace = T) Center <- sample(c("A", "B", "C"), N, replace = T) Time <- sample(c("Morn.", "After.", "Even."), N, replace = T) X <- model.matrix(~ Operators + Center + Time)[, -1] true.beta <- c(0.04, -0.3, 0, 0.2, -0.2) h.fn <- function(x) return(0.00001 * x) queuing <- sim.survdata(N = N, T = 1000, X = X, beta = true.beta, hazard.fun = h.fn) # Kaplan-Meier survival curves fit.Center <- ... plot(fit.Center, xlab = "Seconds", ylab = "Probability of Still Being on Hold", col = c(2, 4, 5)) legend("topright", c("Call Center A", "Call Center B", "Call Center C"), col = c(2, 4, 5), lty = 1) fit.Time <- ... plot(fit.Time, xlab = "Seconds", ylab = "Probability of Still Being on Hold", col = c(2, 4, 5)) legend("topright", c("Morning", "Afternoon", "Evening"), col = c(5, 2, 4), lty = 1) # log-rank test # Cox’s proportional hazards model