Simulate Sample from a Population Grouped by Items
simulate_samples.RdThis function allows you to simulate samples across various sample sizes when the data (optionally) has repeated measures items.
Usage
simulate_samples(
start = 20,
stop = 100,
increase = 5,
population,
replace = TRUE,
nsim = 100,
grouping_items = NULL
)Arguments
- start
Sample size for the smallest potential sample
- stop
Sample size for the largest potential sample
- increase
Number to increase the sample size with for each potential sample
- population
The population data set or the pilot dataset
- replace
A TRUE/FALSE value to simulate with replacement
- nsim
The number of simulations/samples you want to return
- grouping_items
The names of columns to group your data by for the simulation, usually this column is the item column
Examples
# step 1 create data like what I think I'll get or use your own
pops <- simulate_population(mu = 4, mu_sigma = .2, sigma = 2,
sigma_sigma = .2, number_items = 30, number_scores = 20,
smallest_sigma = .02, min_score = 1, max_score = 7, digits = 0)
# step 3 simulate samples
samples <- simulate_samples(start = 20, stop = 100,
increase = 5, population = pops,
replace = TRUE, grouping_items = NULL)
# notice just 20 items
samples[[1]]
#> item score
#> 1 22 5
#> 2 13 5
#> 3 30 4
#> 4 30 5
#> 5 15 6
#> 6 14 2
#> 7 27 4
#> 8 13 3
#> 9 3 6
#> 10 11 4
#> 11 26 4
#> 12 9 3
#> 13 7 5
#> 14 19 5
#> 15 12 1
#> 16 15 5
#> 17 23 4
#> 18 30 3
#> 19 17 5
#> 20 24 3
samples <- simulate_samples(start = 20, stop = 100,
increase = 5, population = pops,
replace = TRUE, grouping_items = "item")
# notice 20 rows per item
samples[[1]]
#> # A tibble: 600 × 2
#> # Groups: item [30]
#> item score
#> <int> <dbl>
#> 1 1 5
#> 2 1 1
#> 3 1 6
#> 4 1 3
#> 5 1 6
#> 6 1 6
#> 7 1 6
#> 8 1 1
#> 9 1 6
#> 10 1 1
#> # ℹ 590 more rows