How do I generate 5000 synthetic data sets in R with 1000 observations in each that are gaussian;

Question

for each I need to For each data set, set σ 2 = 10 and µj = j, where j = 1, . . . , 5, 000 is the index of a data set.

This seems like homework. What have you done so far to try solving this yourself? — camille, Dec 04 '19 at 18:18
That's fine, but revisit the guidance for posting a [mcve]. It's expected that your question has some level of an example, code, expected output, research, etc — camille, Dec 05 '19 at 19:38

score 1 · Answer 1 · answered Dec 04 '19 at 18:17

1

We can use lapply to loop through 1 to 5000 and design a simple function to apply the data to the rnorm function.

lapply(1:5000, function(x) rnorm(n = 1000, mean = x, sd = sqrt(10)))

answered Dec 04 '19 at 18:17

www

score 0 · Answer 2 · answered Dec 04 '19 at 18:23

0

You can use purrr::map().

map(1:5000, ~ rnorm(n = 10000, mean = .x, sd = 10))

If you want to iterate over two different arguments to rnorm:

n_arg <- c(rep(10000, 2500), rep(20000, 2500))
map2(1:5000, n_arg, ~ rnorm(n = .y, mean = .x, sd = 10))

answered Dec 04 '19 at 18:23

Giovanni Colitti

2 Answers2