simulate_samples: Simulate Sample from a Population Grouped by Items

In SemanticPriming/semanticprimeR: Semantic Priming Across Many Languages and Related Projects

Simulate Sample from a Population Grouped by Items

Description

This 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

Value

samples

The simulated samples for each tested sample size.

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]]

samples <- simulate_samples(start = 20, stop = 100,
  increase = 5, population = pops,
  replace = TRUE, grouping_items = "item")

# notice 20 rows per item
samples[[1]]

SemanticPriming/semanticprimeR documentation built on Dec. 5, 2024, 7:59 p.m.