Description Usage Arguments Value Note Author(s) Examples

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

1 2 | ```
simulateNumeric(n, corWithin, corAcross = 0)
simulateBinary(n, corWithin, corAcross = 0)
``` |

`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. |

Returns the Cramer's V coefficient.

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`

.

Andrey A Shabalin andrey.shabalin@gmail.com

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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)
``` |

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