Description Usage Arguments Details Value References See Also Examples

View source: R/main.R View source: R/RcppExports.R

Simulate quantiles of the multiscale statistics under the null hypothesis.

1 | ```
msQuantile(n, alpha = c(0.1), nsim = 5000, verbose = TRUE, is.sim = (n < 1e4), ...)
``` |

`n` |
number of observations |

`alpha` |
significance level; the (1- |

`nsim` |
numer of Monte Carlo simulations |

`is.sim` |
logical. If |

`verbose` |
logical. If |

`...` |
further arguments passed to function |

Empirically, it turns out that the (1-`alpha`

)-quantile of the multiscale statistic converges fast to that of the limit distribution as the number of samples `n`

increases. Thus, for the sake of computational efficiency, the quantile with `n`

= 10,000 are used by default for that with `n`

> 10,000, which has already been precomputed and stored. Of course, for arbitrary sample size `n`

, one can always simulate the quantile by setting `is.sim = TRUE`

, and use the precomputed value by setting `is.sim = FALSE`

. For a given sample size `n`

, simulations are once computed, and then automatically recorded in main memory for later usage.

A vector of length `length(alpha)`

is returned, the same structure as returned by funtion `quantile`

. See Li et al. (2016) for further details.

Li, H., Munk, A., Sieling, H., and Walther, G. (2016). The essential histogram. arXiv:1612.07216.

1 2 3 4 5 6 7 | ```
n = 100 # number of observations
nsim = 100 # number of simulations
alpha = c(0.1, 0.9) # significance level
q = msQuantile(n, alpha, nsim)
print(q)
``` |

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