# NOT RUN { x <- 1:5 mod1 <- lm(c(1:3, 7, 6) ~ x) S1 <- simulate(mod1, nsim = 4) ## repeat the simulation: .Random.seed <- attr(S1, "seed") identical(S1, simulate(mod1, nsim = 4)) S2 <- simulate(mod1, nsim = 200, seed = 101) rowMeans(S2) # should be about the same as fitted(mod1) ## repeat identically: (sseed <- attr(S2, "seed")) # seed; RNGkind as attribute stopifnot(identical(S2, simulate(mod1, nsim = 200, seed = sseed))) ## To be sure about the proper RNGkind, e.g., after RNGversion("2.7.0") ## first set the RNG kind, then simulate do.call(RNGkind, attr(sseed, "kind")) identical(S2, simulate(mod1, nsim = 200, seed = sseed)) ## Binomial GLM examples yb1 <- matrix(c(4, 4, 5, 7, 8, 6, 6, 5, 3, 2), ncol = 2) modb1 <- glm(yb1 ~ x, family = binomial) S3 <- simulate(modb1, nsim = 4) # each column of S3 is a two-column matrix. x2 <- sort(runif(100)) yb2 <- rbinom(100, prob = plogis(2*(x2-1)), size = 1) yb2 <- factor(1 + yb2, labels = c("failure", "success")) modb2 <- glm(yb2 ~ x2, family = binomial) S4 <- simulate(modb2, nsim = 4) # each column of S4 is a factor # }
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