library(lme4) library(merTools) data(sleepstudy) fm1 <- lmer(Reaction ~ Days + (Days|Subject), data=sleepstudy) PI <- predictInterval(merMod = fm1, newdata = sleepstudy, level = 0.95, n.sims = 1000, stat = "median", type="linear.prediction", include.resid.var = TRUE)
My question is: is there a rule of thumb of what the value of
n.sims should be?
My data contains 20686 rows (i.e. 20686 response variables) and 20 predictors. For such dataset, how many bootstrap samples are required? Is there any plots or papers that I can refer to that explain the number of bootstrap samples as a function of data size?