simulate: Generate Artificial Data for Tests and Illustrations

In shiftR: Fast Enrichment Analysis via Circular Permutations

Generate Artificial Data for Tests and Illustrations

Description

These functions generate two artificial data sets with local dependence of observations.

Usage

simulateNumeric(n, corWithin, corAcross = 0)
simulateBinary(n, corWithin, corAcross = 0)

Arguments

n	Total number of elements in each data set.
corWithin	Correlation of adjacent observations within each data set.
corAcross	Correlation of observations across data sets.

Value

Returns the Cramer's V coefficient.

Note

The simulateNumeric function generates two data sets with elements having standard normal distribution.

The simulateBinary function generates data sets with 0/1 values by thresholding the numeric data sets from simulateNumeric.

The simulatePValues function generates data sets of p-values by applying pnorm to the data sets from simulateNumeric.

Author(s)

Andrey A Shabalin [e-mail]

Examples

n = 100000
sim = simulateNumeric(n, 0.5, 0.3)

# Means should be close to 0 (zero)
mean(sim$data1)
mean(sim$data2)

# Variances should be close to 1
var(sim$data1)
var(sim$data2)

# Correlation of adjacent observations
# should be close to 0.5
cor(sim$data1[-1], sim$data1[-n])
cor(sim$data2[-1], sim$data2[-n])

# Correlation between data sets 
# should be close to 0.3
cor(sim$data1, sim$data2)

shiftR documentation built on Sept. 2, 2026, 1:07 a.m.