Aliases: simulate
### ** Examples 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))
[1] FALSE
S2 <- simulate(mod1, nsim = 200, seed = 101) rowMeans(S2) # should be about the same as
1 2 3 4 5 0.6885691 2.2329771 3.7790352 5.2775859 6.8131451
fitted(mod1)
1 2 3 4 5 0.8 2.3 3.8 5.3 6.8
## repeat identically: (sseed <- attr(S2, "seed")) # seed; RNGkind as attribute
[1] 101 attr(,"kind") attr(,"kind")[[1]] [1] "Mersenne-Twister" attr(,"kind")[[2]] [1] "Inversion" attr(,"kind")[[3]] [1] "Rejection"
stopifnot(identical(S2, simulate(mod1, nsim = 200, seed = sseed))) ## To be sure about the proper RNGkind, e.g., after RNGversion("2.7.0")
Warning in RNGkind("Mersenne-Twister", "Inversion", "Rounding"): non-uniform
'Rounding' sampler used
## first set the RNG kind, then simulate do.call(RNGkind, attr(sseed, "kind")) identical(S2, simulate(mod1, nsim = 200, seed = sseed))
[1] TRUE
## 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